Frame work for expression invariant face recognition system using warping technique

Frame work for expression invariant face recognition system using warping technique

Автор: Deepti Ahlawat, Vijay Nehra, Darshana Hooda

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 7 vol.10, 2018 года.

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Facial expressions, usually has an adverse effect on the performance of a face recognition system. In this investigation, expression invariant face recognition algorithm is presented that converts input face image with an arbitrary expression into its corresponding neutral facial image. In the present study, deep learning algorithm is used to train classifiers for reference key-points, where key-points are located and deep neural network is trained to make the system able to locate the landmarks in test image. Create an intermediate triangular mesh from the test and reference image and then warp it using affine transform and take the average of the normalized faces. To extract the features presented in the result image shift invariant feature extraction technique is used. Finally, results are compared and the recognition accuracy is determined for different expressions. The present work is tested on three different databases: JAFFE, Cohn-Kanade (CK) and Yale database. Experimental results show that the expression invariant face recognition method is very robust to variety of expressions and recognition accuracy is found to be 97.8 %, 96.8% and 95.7% for CK, JAFFE and Yale databases respectively.

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Expression Invariant face recognition, Warping, Shift Invariant Feature Transform

Короткий адрес: https://sciup.org/15015980

IDR: 15015980   |   DOI: 10.5815/ijigsp.2018.07.06

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