Test Accuracy Improvement in Face Recognition Using Convolutional Neural Networks
M V Siva Prasad Chowdary1, M Venkatanarayana2
1M V Siva Prasad Chowdary, M.Tech Student, KSRM College of Engineering, Kadapa (Andhra Pradesh), India.
2Dr. M Venkatanarayana, Dean & Professor, KSRM College of Engineering, Kadapa (Andhra Pradesh), India.
Manuscript received on 15 October 2019 | Revised Manuscript received on 24 October 2019 | Manuscript Published on 02 November 2019 | PP: 2447-2451 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B12860982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1286.0982S1119
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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: Now-a-days face recognition plays a major role in identifying face of the specific person. There are different face recognition algorithms such as Eigenfaces algorithm, Local binary pattern histograms, Fisherfaces algorithm. All these algorithms face the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. In this study, the face recognition using neural network and convolutional neural network (CNN) techniques were utilized and implemented with the help of Python software 3.6.6. It is noticed that the test accuracy is improved against translation, rotation, and scale invariance in face recognition using CNN.
Keywords: Convolutional Neural Network(CNN), Face Recognition, Python Software, Test Accuracy.
Scope of the Article: Pattern Recognition