An Efficient Face Detection and Recognition
Thanh Tan Nguyen Thi1, Khanh Nguyen Trong2
1Dr. Thanh Tan Nguyen Thi, Department of Computer Science and Information Systems, Information Technology, Electric Power University, Hanoi, Vietnam.
2Dr. Khanh Nguyen Trong, Department of Software Engineering, Posts Tele Communications Institute of Technology, Hanoi, Vietnam.
Manuscript received on 8 February 2018 | Revised Manuscript received on 15 February 2018 | Manuscript Published on 28 February 2018 | PP: 35-39 | Volume-7 Issue-5, February 2018 | Retrieval Number: E2497027518/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this article, we propose a new method to effectively recognize faces from connected devices like real-time camera or webcam. The method contains two phases: Detecting and recognizing faces from the webcam frame. The face detection phase uses HOG features and SVM linear classifier. The second phase bases on FaceNet neural network model to automatically extract facial features and SVM classifiers. The experiments with UOF, FEI, JAFFE and LZW dataset is presented to show the efficiency of the proposed method. Experimental results show that the proposed method achieves high accuracy and stability on the test data sets collected from the actual environment.
Keywords: Face Recognition, Real-Time Recognition, Frame based Recognigition, Recognition Deep Neural Network.
Scope of the Article: Pattern Recognition