The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Matti Pietikäinen - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal Local Binary Pattern with revisited integral projection for spontaneous facial micro expression recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works used spatiotemporal Local Binary Pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-expression recognition. In this paper, we propose a discriminative spatiotemporal Local Binary Pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition. First, we revisit an integral projection for preserving the shape attribute of micro-expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with Local Binary Pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
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median robust extended Local Binary Pattern for texture classification
International Conference on Image Processing, 2015Co-Authors: Li Liu, Matti Pietikäinen, Paul Fieguth, Songyang LaoAbstract:Local Binary Patterns (LBP) are among the most computationally efficient amongst high-performance texture features. However, LBP is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper we introduce a novel descriptor for texture classification, the Median Robust Extended Local Binary Pattern (MRELBP). In contrast to traditional LBP and many LBP variants, MRELBP compares Local image medians instead of raw image intensities. We develop a multiscale LBP-type descriptor by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure. A comprehensive evaluation on benchmark datasets reveals MRELBP's remarkable performance (robust to gray scale variations, rotation changes and noise) relative to state-of-the-art algorithms, but nevertheless at a low computational cost, producing the best classification scores of 99.82%, 99.38% and 99.77% on three popular Outex test suites. Furthermore, MRELBP is also shown to be highly robust to image noise including Gaussian noise, Gaussian blur, Salt-and-Pepper noise and random pixel corruption.
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globally rotation invariant multi scale co occurrence Local Binary Pattern
Image and Vision Computing, 2015Co-Authors: Linlin Shen, Guoying Zhao, Matti PietikäinenAbstract:This paper proposes a globally rotation invariant multi-scale co-occurrence Local Binary Pattern (MCLBP) feature for texture-relevant tasks. In MCLBP, we arrange all co-occurrence Patterns into groups according to properties of the co-Patterns, and design three encoding functions (Sum, Moment, and Fourier Pooling) to extract features from each group. The MCLBP can effectively capture the correlation information between different scales and is also globally rotation invariant (GRI). The MCLBP is substantially different from most existing LBP variants including the LBP, the CLBP, and the MSJ-LBP that achieves rotation invariance by Locally rotation invariant (LRI) encoding. We fully evaluate the properties of the MCLBP and compare it with some powerful features on five challenging databases. Extensive experiments demonstrate the effectiveness of the MCLBP compared to the state-of-the-art LBP variants including the CLBP and the LBPHF. Meanwhile, the dimension and computational cost of the MCLBP is also lower than that of the CLBP_S/M/C and LBPHF_S_M. This paper proposes a globally rotation invariant multi-scale co-occurrence of LBPs (MCLBP).The proposed MCLBP can effectively capture the correlation between the LBPs in different scales.Three globally rotation invariant encoding methods are introduced for MCLBP.The proposed MCLBP performs very well on texture, material, and medical cell classification.
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riesz based volume Local Binary Pattern and a novel group expression model for group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the Local Binary Pattern descriptor, named Riesz-based volume Local Binary Pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and Local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.
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age estimation using Local Binary Pattern kernel density estimate
International Conference on Image Analysis and Processing, 2013Co-Authors: Juha Ylioinas, Xiaopeng Hong, Abdenour Hadid, Matti PietikäinenAbstract:We propose a novel kernel method for constructing Local Binary Pattern statistics for facial representation in human age estimation. For age estimation, we make use of the de facto support vector regression technique. The main contributions of our work include (i) evaluation of a pose correction method based on simple image flipping and (ii) a comparison of two Local Binary Pattern based facial representations, namely a spatially enhanced histogram and a novel kernel density estimate. Our single- and cross-database experiments indicate that the kernel density estimate based representation yields better estimation accuracy than the corresponding histogram one, which we regard as a very interesting finding. In overall, the constructed age estimation system provides comparable performance against the state-of-the-art methods. We are using a well-defined evaluation protocol allowing a fair comparison of our results.
Guoying Zhao - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal Local Binary Pattern with revisited integral projection for spontaneous facial micro expression recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works used spatiotemporal Local Binary Pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-expression recognition. In this paper, we propose a discriminative spatiotemporal Local Binary Pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition. First, we revisit an integral projection for preserving the shape attribute of micro-expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with Local Binary Pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
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facial micro expression recognition using spatiotemporal Local Binary Pattern with integral projection
International Conference on Computer Vision, 2015Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Matti PiteikainenAbstract:Recently, there are increasing interests in inferring mirco-expression from facial image sequences. For micro-expression recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-expressions. Furthermore, we employ the Local Binary Pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-expression recognition.
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globally rotation invariant multi scale co occurrence Local Binary Pattern
Image and Vision Computing, 2015Co-Authors: Linlin Shen, Guoying Zhao, Matti PietikäinenAbstract:This paper proposes a globally rotation invariant multi-scale co-occurrence Local Binary Pattern (MCLBP) feature for texture-relevant tasks. In MCLBP, we arrange all co-occurrence Patterns into groups according to properties of the co-Patterns, and design three encoding functions (Sum, Moment, and Fourier Pooling) to extract features from each group. The MCLBP can effectively capture the correlation information between different scales and is also globally rotation invariant (GRI). The MCLBP is substantially different from most existing LBP variants including the LBP, the CLBP, and the MSJ-LBP that achieves rotation invariance by Locally rotation invariant (LRI) encoding. We fully evaluate the properties of the MCLBP and compare it with some powerful features on five challenging databases. Extensive experiments demonstrate the effectiveness of the MCLBP compared to the state-of-the-art LBP variants including the CLBP and the LBPHF. Meanwhile, the dimension and computational cost of the MCLBP is also lower than that of the CLBP_S/M/C and LBPHF_S_M. This paper proposes a globally rotation invariant multi-scale co-occurrence of LBPs (MCLBP).The proposed MCLBP can effectively capture the correlation between the LBPs in different scales.Three globally rotation invariant encoding methods are introduced for MCLBP.The proposed MCLBP performs very well on texture, material, and medical cell classification.
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riesz based volume Local Binary Pattern and a novel group expression model for group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the Local Binary Pattern descriptor, named Riesz-based volume Local Binary Pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and Local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.
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quantifying micro expressions with constraint Local model and Local Binary Pattern
European Conference on Computer Vision, 2014Co-Authors: Wenjing Yan, Sujing Wang, Yuhsin Chen, Guoying ZhaoAbstract:Micro-expression may reveal genuine emotions that people try to conceal. However, it’s difficult to measure it. We selected two feature extraction methods to analyze micro-expressions by assessing the dynamic information. The Constraint Local Model (CLM) algorithm is employed to detect faces and track feature points. Based on these points, the ROIs (Regions of Interest) on the face are drawn for further analysis. In addition, Local Binary Pattern (LBP) algorithm is employed to extract texture information from the ROIs and measure the differences between frames. The results from the proposed methods are compared with manual coding. These two proposed methods show good performance, with sensitivity and reliability. This is a pilot study on quantifying micro-expression movement for psychological research purpose. These methods would assist behavior researchers in measuring facial movements on various facets and at a deeper level.
Xiaohua Huang - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal Local Binary Pattern with revisited integral projection for spontaneous facial micro expression recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works used spatiotemporal Local Binary Pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-expression recognition. In this paper, we propose a discriminative spatiotemporal Local Binary Pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition. First, we revisit an integral projection for preserving the shape attribute of micro-expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with Local Binary Pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
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facial micro expression recognition using spatiotemporal Local Binary Pattern with integral projection
International Conference on Computer Vision, 2015Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Matti PiteikainenAbstract:Recently, there are increasing interests in inferring mirco-expression from facial image sequences. For micro-expression recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-expressions. Furthermore, we employ the Local Binary Pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-expression recognition.
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riesz based volume Local Binary Pattern and a novel group expression model for group happiness intensity analysis
British Machine Vision Conference, 2015Co-Authors: Xiaohua Huang, Roland Goecke, Abhinav Dhall, Guoying Zhao, Matti PietikäinenAbstract:Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the Local Binary Pattern descriptor, named Riesz-based volume Local Binary Pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and Local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.
Sujing Wang - One of the best experts on this subject based on the ideXlab platform.
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discriminative spatiotemporal Local Binary Pattern with revisited integral projection for spontaneous facial micro expression recognition
IEEE Transactions on Affective Computing, 2019Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Xiaoyi Feng, Matti PietikäinenAbstract:Recently, there have been increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works used spatiotemporal Local Binary Pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-expression recognition. In this paper, we propose a discriminative spatiotemporal Local Binary Pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition. First, we revisit an integral projection for preserving the shape attribute of micro-expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with Local Binary Pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
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facial micro expression recognition using spatiotemporal Local Binary Pattern with integral projection
International Conference on Computer Vision, 2015Co-Authors: Xiaohua Huang, Guoying Zhao, Sujing Wang, Matti PiteikainenAbstract:Recently, there are increasing interests in inferring mirco-expression from facial image sequences. For micro-expression recognition, feature extraction is an important critical issue. In this paper, we proposes a novel framework based on a new spatiotemporal facial representation to analyze micro-expressions with subtle facial movement. Firstly, an integral projection method based on difference images is utilized for obtaining horizontal and vertical projection, which can preserve the shape attributes of facial images and increase the discrimination for micro-expressions. Furthermore, we employ the Local Binary Pattern operators to extract the appearance and motion features on horizontal and vertical projections. Intensive experiments are conducted on three available published micro-expression databases for evaluating the performance of the method. Experimental results demonstrate that the new spatiotemporal descriptor can achieve promising performance in micro-expression recognition.
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quantifying micro expressions with constraint Local model and Local Binary Pattern
European Conference on Computer Vision, 2014Co-Authors: Wenjing Yan, Sujing Wang, Yuhsin Chen, Guoying ZhaoAbstract:Micro-expression may reveal genuine emotions that people try to conceal. However, it’s difficult to measure it. We selected two feature extraction methods to analyze micro-expressions by assessing the dynamic information. The Constraint Local Model (CLM) algorithm is employed to detect faces and track feature points. Based on these points, the ROIs (Regions of Interest) on the face are drawn for further analysis. In addition, Local Binary Pattern (LBP) algorithm is employed to extract texture information from the ROIs and measure the differences between frames. The results from the proposed methods are compared with manual coding. These two proposed methods show good performance, with sensitivity and reliability. This is a pilot study on quantifying micro-expression movement for psychological research purpose. These methods would assist behavior researchers in measuring facial movements on various facets and at a deeper level.
Syakira Nurina Shaputri - One of the best experts on this subject based on the ideXlab platform.
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KLASIFIKASI LOVEBIRD BERDASARKAN BENTUK KEPALA DAN WARNA DENGAN METODE Local Binary Pattern (LBP) DAN FUZZY LOGIC
Universitas Telkom, 2015Co-Authors: Syakira Nurina ShaputriAbstract:ABSTRAK Pada masa ini banyak hobi yang mulai di kembangankan menjadi bisnis. Salah satunya sedang marak menjadi pembicaraan adalah hobi memelihara burung lovebird. Selain suaranya yang nyaring dan panjang lovebird juga memiliki warna bulu yang indah. Tidak hanya menjadi hobi lovebird juga diperuntukkan sebagai burung kontes. Selain kontes kicau lovebird baru-baru ini mulai muncul kontes kecantikan lovebird yang mencakup beberapa aspek seperti warna bulu, bentuk kepala dan ekor. Indonesia cukup tertinggal dibanding negara lain yang telah terlebih dahulu menjadikan lovebird sebagai burung kontes kecantikan. Penilaian dilakukan oleh beberapa juri dengan standarisasi yang telah ditentukan oleh forum komunitas lovebird Indonesia. Pengolahan citra digital dibutuhkan agar hasil penilaian juri lebih objektif. Pada tugas akhir ini dibuat sistem yang dapat mengklasifikasikan kualitas lovebird berdasarkan bentuk kepala dan warna pada bagian leher dengan memanfaatkan pengolahan citra. Metode yang digunakan dalam sistem ini adalah Local Binary Pattern (LBP) untuk mendapatkan ciri dari setiap warna. Untuk proses klasifikasi digunakan metode Fuzzy Logic. Dengan jumlah sampel sebanyak 15 data latih dan 30 data uji. Hasil penelitian tugas akhir didapatkan nilai akurasi tertinggi sebesar 93.3% untuk pengujian bentuk kepala dengan waktu komputasi total 20.7627 detik, 83.3% untuk pengujian warna bulu leher dengan waktu komputasi 55.787 detik, dan 80% untuk pengujian kepala dan warna dengan waktu komputasi 44.9024 detik. Diharapkan dengan kemampuan sistem ini dapat membantu para juri dan pecinta lovebird sehingga dapat dijadikan standart untuk penilaian kualiatas lovebird. Kata kunci : Lovebird, Local Binary Pattern (LBP), fuzzy logic
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klasifikasi lovebird berdasarkan bentuk kepala dan warna dengan metode Local Binary Pattern lbp dan fuzzy logic
eProceedings of Engineering, 2015Co-Authors: Syakira Nurina Shaputri, Bambang Hidayat, Unang SunaryaAbstract:ABSTRAK Kontes kecantikan lovebird mulai berkembang di Indonesia. Beberapa aspek yang menjadi penilaian adalah bentuk kepala, warna, bentuk dada, ekor, harmonisasi, dan tingkah laku. Penilaian dilakukan oleh beberapa juri dengan standarisasi yang telah ditentukan oleh forum komunitas lovebird Indonesia. Pada penelitian ini dibuat sistem yang dapat mengklasifikasikan kualitas lovebird berdasarkan bentuk kepala dan warna pada bagian leher dengan memanfaatkan pengolahan citra. Metode yang digunakan dalam sistem ini adalah Local Binary Pattern (LBP) untuk mendapatkan ciri warna. Untuk proses klasifikasi digunakan metode Fuzzy Logic. Dengan jumlah sampel sebanyak 15 data latih dan 30 data uji. Hasil penelitian penelitian didapatkan nilai akurasi tertinggi sebesar 93.3% untuk pengujian bentuk kepala dengan waktu komputasi total 20.7627 detik, 83.3% untuk pengujian warna dengan waktu komputasi 55.787 detik, dan 80% untuk pengujian kepala dan warna dengan waktu komputasi 44.9024 detik. Kata Kunci : Lovebird, Local Binary Pattern (LBP), fuzzy logic.
