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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Subject: search.filters.subject.Face recognition

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Now showing 1 - 10 of 10
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    A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications
    (1970-01-01)
    Chou, Kuang Pen
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    Prasad, Mukesh
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    Yang, Jie
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    Su, Sheng-Yao
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    Tao, Xian
    Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
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    Deep Sparse Representation Classifier for facial recognition and detection system
    (2019-07-01)
    Cheng, Eric-Juwei
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    Chou, Kuang-Pen
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    Rajora, Shantanu
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    Jin, Bo-Hao
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    Tanveer, M.
    This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets. (C) 2019 Elsevier B.V. All rights reserved.
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    A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
    (2017-01-01)
    Wang, Ping
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    Lin, Wen-Hui
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    Chao, Kuo-Ming
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    Lo, Chi-Chun
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    資訊管理與財務金融系 註：原資管所+財金所
    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 gamma located in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting gamma = 1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.
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    LEARNING GUIDED CONVOLUTIONAL NEURAL NETWORKS FOR CROSS-RESOLUTION FACE RECOGNITION
    (2017-01-01)
    Fu, Tzu-Chien
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    Chiu, Wei-Chen
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    Wang, Yu-Chiang Frank
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    交大名義發表
    ;
    National Chiao Tung University
    Cross-resolution face recognition tackles the problem of matching face images with different resolutions. Although state-of-the-art convolutional neural network (CNN) based methods have reported promising performances on standard face recognition problems, such models cannot sufficiently describe images with resolution different from those seen during training, and thus cannot solve the above task accordingly. In this paper, we propose Guided Convolutional Neural Network (Guided-CNN), which is a novel CNN architecture with parallel sub-CNN models as guide and learners. Unique loss functions are introduced, which would serve as joint supervision for images within and across resolutions. Our experiments not only verify the use of our model for cross-resolution recognition, but also its applicability of recognizing face images with different degrees of occlusion.
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    類神經網路於生物測定認證技術及應用之研究---總計畫
    (1997)
    傅心家
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    Fu Hsin-Chia
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    交通大學資訊工程系
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    利用差分影像來作灰階人臉辨識
    (1998)
    莊仁輝
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    JEN-HUICHUANG
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    交通大學資訊科學系
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    雙眼彩色臉孔辨識之研究
    (1999)
    陳玲慧
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    CHEN LING-HWEI
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    交通大學資訊科學研究所
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    Accuracy enhanced thermal face recognition
    (2013-11-01)
    Lin, Chun-Fu
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    Lin, Sheng-Fuu
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    電控工程研究所
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    Institute of Electrical and Control Engineering
    Human face recognition has been generally researched for the last three decades. Face recognition with thermal image has begun to attract significant attention gradually since illumination of environment would not affect the recognition performance. However, the recognition performance of traditional thermal face recognizer is still insufficient in practical application. This study presents a novel thermal face recognizer employing not only thermal features but also critical facial geometric features which would not be influenced by hair style to improve the recognition performance. A three-layer back-propagation feed-forward neural network is applied as the classifier. Traditional thermal face recognizers only use the indirect information of the topography of blood vessels like thermogram as features. To overcome this limitation, the proposed thermal face recognizer can use not only the indirect information but also the direct information of the topography of blood vessels which is unique for every human. Moreover, the recognition performance of the proposed thermal features would not decrease even if the hair of frontal bone varies, the eye blinks or the nose breathes. Experimental results show that the proposed features are significantly more effective than traditional thermal features and the recognition performance of thermal face recognizer is improved. (C) 2013 Elsevier B.V. All rights reserved.