Face Recognition Based on Windowing Techniques, with Compressed Hybrid Domain Features
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 10 |
| Year of Publication : 2023 |
| Authors : M. Niranjana Kumara, A. Divya, K.B. Raja, K.R. Venugopal |
10.14445/23488549/IJECE-V10I10P107
How to Cite?
M. Niranjana Kumara, A. Divya, K.B. Raja, K.R. Venugopal, "Face Recognition Based on Windowing Techniques, with Compressed Hybrid Domain Features," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 10, pp. 64-75, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I10P107
Abstract:
Face recognition is used in human-machine interfaces for day-to-day activities in real-time applications. This paper proposes face recognition based on windowing techniques with compressed hybrid domain features. The face images are resized to 224×160 and are segmented into windows of sizes 4 × 4, 8 × 8, and 16 × 16. The hybrid features are extracted using Discrete Wavelet Transform (DWT) and the covariance concept on each window. The average covariance of each window is computed to obtain compressed final features. Artificial Neural Network (ANN) is used for classification to identify a person effectively. The obtained results of average recognition are around 98% for four benchmarked public face databases. It is observed that the proposed model attains a higher recognition rate with a reduced number of features compared with the existing methods.
Keywords:
ANN, Biometrics, Covariance, DWT, Face Recognition.
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