Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (26)
  • Open Access

    REVIEW

    Survey on the Loss Function of Deep Learning in Face Recognition

    Jun Wang1, Suncheng Feng2,*, Yong Cheng3, Najla Al-Nabhan4

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 29-45, 2021, DOI:10.32604/jihpp.2021.016835

    Abstract With the continuous development of face recognition network, the selection of loss function plays an increasingly important role in improving accuracy. The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance. So far, one of our mainstream loss function optimization methods is to add penalty terms, such as orthogonal loss, to further constrain the original loss function. The other is to optimize using the loss based on angular/cosine margin. The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT More >

  • Open Access

    ARTICLE

    Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

    Masoud Muhammed Hassan1,*, Haval Ismael Hussein1, Adel Sabry Eesa1, Ramadhan J. Mstafa1,2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1637-1659, 2021, DOI:10.32604/cmc.2021.016467

    Abstract Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature… More >

  • Open Access

    ARTICLE

    An Adversarial Attack System for Face Recognition

    Yuetian Wang, Chuanjing Zhang, Xuxin Liao, Xingang Wang, Zhaoquan Gu*

    Journal on Artificial Intelligence, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jai.2021.014175

    Abstract Deep neural networks (DNNs) are widely adopted in daily life and the security problems of DNNs have drawn attention from both scientific researchers and industrial engineers. Many related works show that DNNs are vulnerable to adversarial examples that are generated with subtle perturbation to original images in both digital domain and physical domain. As a most common application of DNNs, face recognition systems are likely to cause serious consequences if they are attacked by the adversarial examples. In this paper, we implement an adversarial attack system for face recognition in both digital domain that generates More >

  • Open Access

    ARTICLE

    An Efficient Adaptive Network-Based Fuzzy Inference System with Mosquito Host-Seeking For Facial Expression Recognition

    M. Carmel Sobia1, A. Abudhahir2

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 869-881, 2018, DOI:10.31209/2018.100000014

    Abstract In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB. More >

  • Open Access

    ARTICLE

    Optimization of Face Recognition System Based on Azure IoT Edge

    Shen Li1, Fang Liu1,*, Jiayue Liang1, Zhenhua Cai1, Zhiyao Liang2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1377-1389, 2019, DOI:10.32604/cmc.2019.06402

    Abstract With the rapid development of artificial intelligence, face recognition systems are widely used in daily lives. Face recognition applications often need to process large amounts of image data. Maintaining the accuracy and low latency is critical to face recognition systems. After analyzing the two-tier architecture “client-cloud” face recognition systems, it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded, and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network. This paper proposes a flexible and… More >

  • Open Access

    ARTICLE

    A Face Recognition Algorithm Based on LBP-EHMM

    Tao Li1, Lingyun Wang1, Yin Chen1,*, Yongjun Ren1, Lei Wang1, Jinyue Xia2

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 59-68, 2019, DOI:10.32604/jai.2019.06346

    Abstract In order to solve the problem that real-time face recognition is susceptible to illumination changes, this paper proposes a face recognition method that combines Local Binary Patterns (LBP) and Embedded Hidden Markov Model (EHMM). Face recognition method. The method firstly performs LBP preprocessing on the input face image, then extracts the feature vector, and finally sends the extracted feature observation vector to the EHMM for training or recognition. Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate. More >

Displaying 21-30 on page 3 of 26. Per Page