Face Recognition Approach

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Chi-chun Lo - One of the best experts on this subject based on the ideXlab platform.

  • ICEBE - A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
    2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2020
    Co-Authors: Ping Wang, Kuo-ming Chao, Chi-chun Lo
    Abstract:

    Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous Approaches including Face Recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the Face Recognition problem focus mainly on the static analysis to determine the Face Recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the Face Recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a Face Recognition process under the situation of vague facial features using deep reinforcement learning (DRL) Approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of Face Recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of Face Recognition (100% at gamma correction γlocated in [0.5, 1.6]) compared with the average precision for Face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.

  • A Cross-Age Face Recognition Approach Using Fog Computing Architecture for User Authentication on Mobile Devices
    2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), 2018
    Co-Authors: Ping Wang, Kuo-ming Chao, Bao-hua Wu, Chi-chun Lo
    Abstract:

    Mobile commerce security needs quality services where mobile devices respond in real-time and traditional cloud computing Approach uses a centralized architecture does not support these systems with such time dependency well. Thus, this study proposes a cross-age Face-Recognition model with MobileNets to determine the identity of a user in the situation of aging appearance change on a fog computing architecture. Especially, this study uses a Face-Recognition technique based on a distributed concept. In this concept, MobileNet-based Face Recognition system effectively performs cross-age Face Recognition for identity authentication with the enhanced facial features of the users locally. Overall, the MobileNet model reaches acceptable prediction accuracy with lower latency in Face Recognition process compared with that of the existing CNNs schemes.

  • A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
    2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2017
    Co-Authors: Ping Wang, Kuo-ming Chao, Chi-chun Lo
    Abstract:

    Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous Approaches including Face Recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the Face Recognition problem focus mainly on the static analysis to determine the Face Recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the Face Recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a Face Recognition process under the situation of vague facial features using deep reinforcement learning (DRL) Approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of Face Recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of Face Recognition (100% at gamma correction γ located in [0.5, 1.6]) compared with the average precision for Face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.

Mohammed Bennamoun - One of the best experts on this subject based on the ideXlab platform.

  • an efficient 3d Face Recognition Approach using local geometrical signatures
    Pattern Recognition, 2014
    Co-Authors: Mohammed Bennamoun, Munawar Hayat
    Abstract:

    This paper presents a computationally efficient 3D Face Recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the Face. Kernel Principal Component Analysis (KPCA) is then used to extract the mid-level features from the extracted ARSs to improve the discriminative power. The mid-level features are then concatenated into a single feature vector and fed into a Support Vector Machine (SVM) to perform Face Recognition. The proposed Approach addresses the expression variation problem by using facial scans with various expressions of different individuals for training. We conducted a number of experiments on the Face Recognition Grand Challenge (FRGC v2.0) and the 3D track of Shape Retrieval Contest (SHREC 2008) datasets, and a superior Recognition performance has been achieved. Our experimental results show that the proposed system achieves very high Verification Rates (VRs) of 97.8% and 88.5% at a 0.1% False Acceptance Rate (FAR) for the "neutral vs. nonneutral" experiments on the FRGC v2.0 and the SHREC 2008 datasets respectively, and 96.7% for the ROC III experiment of the FRGC v2.0 dataset. Our experiments also demonstrate the computational efficiency of the proposed Approach. HighlightsNovel facial Angular Radial Signatures (ARSs) are proposed for 3D Face Recognition.The Signatures are extracted from the semi-rigid facial regions.A two-stage mapping-based classification strategy is used to perform Face Recognition.ARSs combined with machine learning techniques can handle expression variations.State-of-the-art performance on two public datasets with high efficiency is achieved.

  • an efficient 3d Face Recognition Approach based on the fusion of novel local low level features
    Pattern Recognition, 2013
    Co-Authors: Mohammed Bennamoun, Amar A Elsallam
    Abstract:

    We present a novel 3D Face Recognition Approach based on low-level geometric features that are collected from the eyes-forehead and the nose regions. These regions are relatively less influenced by the deformations that are caused by facial expressions. The extracted features revealed to be efficient and robust in the presence of facial expressions. A region-based histogram descriptor computed from these features is used to uniquely represent a 3D Face. A Support Vector Machine (SVM) is then trained as a classifier based on the proposed histogram descriptors to recognize any test Face. In order to combine the contributions of the two facial regions (eyes-forehead and nose), both feature-level and score-level fusion schemes have been tested and compared. The proposed Approach has been tested on FRGC v2.0 and BU-3DFE datasets through a number of experiments and a high Recognition performance was achieved. Based on the results of ''neutral vs. non-neutral'' experiment of FRGC v2.0 and ''low-intensity vs. high-intensity'' experiment of BU-3DFE, the feature-level fusion scheme achieved verification rates of 97.6% and 98.2% at 0.1% False Acceptance Rate (FAR) and identification rates of 95.6% and 97.7% on the two datasets respectively. The experimental results also have shown that the feature-level fusion scheme outperformed the score-level fusion one.

  • IVCNZ - A structured template based 3D Face Recognition Approach
    Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12, 2012
    Co-Authors: Mohammed Bennamoun, Amar A. El-sallam
    Abstract:

    There are many challenges to achieve 2D Face Recognition including illumination, expression, and pose variations. However, the human Face provides not only 2D texture but also rich 3D shape information. In this work, we present a novel 3D Face Recognition Approach based on a new proposed concept termed structured template in analogy with the structured light Approach. Our Approach excludes the non-rigid facial region which is most affected by facial expressions. We first apply the structured template on the facial range image to extract 20 levels of stripes and convert them to pointclouds. Then we can represent a 3D facial scan by 20 levels of 3D open curves. As a result we can match the shape of two facial scans by matching the shape of their corresponding open curves. An open curve analysis algorithm is applied to calculate the geodesic distance between a pair of open curves extracted from different Faces. The geodesic distance is then used as a similarity measure and two facial scans can be matched using the sum of all levels of their corresponding geodesic distance. Experiments are performed on the FRGC v2.0 dataset which demonstrate excellent performance.

Shiguang Shan - One of the best experts on this subject based on the ideXlab platform.

  • a fast and robust 3d Face Recognition Approach based on deeply learned Face representation
    Neurocomputing, 2019
    Co-Authors: Menglong Yang, Shiguang Shan
    Abstract:

    Abstract With the superiority of three-dimensional (3D) scanning data, e.g., illumination invariance and pose robustness, 3D Face Recognition theoretically has the potential to achieve better results than two-dimensional (2D) Face Recognition. However, traditional 3D Face Recognition techniques suffer from high computational costs. This paper proposes a fast and robust 3D Face Recognition Approach with three component technologies: a fast 3D scan preprocessing, multiple data augmentation, and a deep learning technique based on facial component patches. First, unlike the majority of the existing Approaches, which require accurate facial registration, the proposed Approach uses only three facial landmarks. Second, the specifical deep network with an improved supervision is designed to extract complementary features from four overlapping facial component patches. Finally, a data augmentation technique and three self-collected 3D Face datasets are used to enlarge the scale of the training data. The proposed Approach outperforms the state-of-the-art algorithms on four public 3D Face benchmarks, i.e., 100%, 99.75%, 99.88%, and 99.07% rank-1 IRs with the standard test protocol on the FRGC v2.0, Bosphorus, BU-3DFE, and 3D-TEC datasets, respectively. Further, it requires only 0.84 seconds to identify a probe from a gallery with 466 Faces.

Jean-luc Dugelay - One of the best experts on this subject based on the ideXlab platform.

  • BTAS - Block based Face Recognition Approach robust to nose alterations
    2012 IEEE Fifth International Conference on Biometrics: Theory Applications and Systems (BTAS), 2012
    Co-Authors: Neslihan Kose, Nesli Erdogmus, Jean-luc Dugelay
    Abstract:

    Face Recognition that is robust to alterations applied on Face via plastic surgery or prosthetic make-up can be still considered as a new topic. In this paper, a block based Face analysis Approach is proposed which provides a fairly good Recognition performance together with the advantage of robustness to such kind of alterations. For this study, a simulated nose alteration Face database is used which is prepared using FRGC v1.0. Since this is a 3D database, the Approach can be tested both in 2D and 3D, which is one of the contributions of this study. Furthermore, differently from previous works, baseline results for the original database are reported. The impact which is purely due to the applied nose alterations is measured using both the proposed Approach and the standard techniques which are based on holistic description for comparison. The results indicate that although both 2D and 3D modalities lose precision due to alterations, the proposed Approach is superior in terms of both the Recognition performance and robustness to alterations compared to standard techniques.

  • Block based Face Recognition Approach robust to nose alterations
    2012 IEEE Fifth International Conference on Biometrics: Theory Applications and Systems (BTAS), 2012
    Co-Authors: Neslihan Kose, Nesli Erdogmus, Jean-luc Dugelay
    Abstract:

    Face Recognition that is robust to alterations applied on Face via plastic surgery or prosthetic make-up can be still considered as a new topic. In this paper, a block based Face analysis Approach is proposed which provides a fairly good Recognition performance together with the advantage of robustness to such kind of alterations. For this study, a simulated nose alteration Face database is used which is prepared using FRGC v1.0. Since this is a 3D database, the Approach can be tested both in 2D and 3D, which is one of the contributions of this study. Furthermore, differently from previous works, baseline results for the original database are reported. The impact which is purely due to the applied nose alterations is measured using both the proposed Approach and the standard techniques which are based on holistic description for comparison. The results indicate that although both 2D and 3D modalities lose precision due to alterations, the proposed Approach is superior in terms of both the Recognition performance and robustness to alterations compared to standard techniques.

Ping Wang - One of the best experts on this subject based on the ideXlab platform.

  • ICEBE - A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
    2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2020
    Co-Authors: Ping Wang, Kuo-ming Chao, Chi-chun Lo
    Abstract:

    Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous Approaches including Face Recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the Face Recognition problem focus mainly on the static analysis to determine the Face Recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the Face Recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a Face Recognition process under the situation of vague facial features using deep reinforcement learning (DRL) Approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of Face Recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of Face Recognition (100% at gamma correction γlocated in [0.5, 1.6]) compared with the average precision for Face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.

  • A Cross-Age Face Recognition Approach Using Fog Computing Architecture for User Authentication on Mobile Devices
    2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), 2018
    Co-Authors: Ping Wang, Kuo-ming Chao, Bao-hua Wu, Chi-chun Lo
    Abstract:

    Mobile commerce security needs quality services where mobile devices respond in real-time and traditional cloud computing Approach uses a centralized architecture does not support these systems with such time dependency well. Thus, this study proposes a cross-age Face-Recognition model with MobileNets to determine the identity of a user in the situation of aging appearance change on a fog computing architecture. Especially, this study uses a Face-Recognition technique based on a distributed concept. In this concept, MobileNet-based Face Recognition system effectively performs cross-age Face Recognition for identity authentication with the enhanced facial features of the users locally. Overall, the MobileNet model reaches acceptable prediction accuracy with lower latency in Face Recognition process compared with that of the existing CNNs schemes.

  • A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
    2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2017
    Co-Authors: Ping Wang, Kuo-ming Chao, Chi-chun Lo
    Abstract:

    Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous Approaches including Face Recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the Face Recognition problem focus mainly on the static analysis to determine the Face Recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the Face Recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a Face Recognition process under the situation of vague facial features using deep reinforcement learning (DRL) Approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of Face Recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of Face Recognition (100% at gamma correction γ located in [0.5, 1.6]) compared with the average precision for Face image (approximately 99.5% at normal lighting γ=1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.