Computer Science ›› 2020, Vol. 47 ›› Issue (9): 163-168.doi: 10.11896/jsjkx.190900118

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Fast Face Recognition Based on Deep Learning and Multiple Hash Similarity Weighting

DENG Liang1, XU Geng-lin1, LI Meng-jie1, CHEN Zhang-jin1,2   

  1. 1 Microelectronics Research and Development Center,Shanghai University,Shanghai 200444,China
    2 Computer Center,Shanghai University,Shanghai 200444,China
  • Received:2019-09-18 Published:2020-09-10
  • About author:DENG Liang,born in 1996,master.His main research interests include digital chip design,deep learning,and face recognition.
    CHEN Zhang-jin,born in 1969,doctor,professor.His main research interests include digital chip design,large-screen LED display research and development.
  • Supported by:
    National Natural Science Foundation of China (61674100).

Abstract: Whether using the traditional method or neural network for face recognition,there are problems of large computation and long computation time.It is difficult to detect and match the faces in the video in real time.Aiming at the above problems,lightweight neural network is used for face detection,simple hash algorithm is used to calculate the similarity of face images,and multiple hash similarity values are weighted for face matching.It is a feasible scheme to reduce computation time and realize fast face recognition.The lightweight neural network Mobilenet is used as the face feature extraction network,and the pruned SSD model is used as the detection network.The face detection is realized by cascading Mobilenet and SSD,and then the detected face image is recognized.Firstly,the mean hash similarity and the perceived hash similarity of the face images are calculated separately.Then,taking α and β as weighted coefficients of the mean hash and the perceived hash respectively,the mean hash and perceived hash similarity value of the image are weighted,and the result is taken as the final similarity of the image.When the weighted similarity value is greater than the set threshold I,it is considered to be the same person.When the weighted similarity value is less than the set threshold K,it is considered to be a different person.For images whose similarity is between thresholds I and K,they are optimally matched in order of similarity values from high to low.The face detection accuracy rate of the proposed method on WiderFace and FDDB reaches 92.5% and 94.2% respectively,and the average processing time per image is 56ms.The accuracy of face matching in the ORL standard face database reaches 96.2%.When camera is used for real-time face recognition test,the face recognition accuracy of the proposed method is 95%,and the average face recognition speed is 80ms.It has been proved by experiments that real-time face detection and matching can be realized under the premise of ensuring high accuracy.

Key words: Face detection, Deep learning, Hash algorithm, Face matching

CLC Number: 

  • TP183
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