Human Face Detection and Recognition using Web-Cam
- 1 Mahasarakham UniversityKamreang, Thailand
Copyright: © 2020 Petcharat Pattanasethanon and Charuay Savithi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: The illuminance insensitivity that reflects the angle of human facial aspects occurs once the distance between the object and the camera is too different such as animated images. This has been a problem for facial recognition system for decades. Approach: For this reason, our study represents a novel technique for facial recognition through the implementation of Successes Mean Quantization Transform and Spare Network of Winnow with the assistance of Eigenface computation. After having limited the frame of the input image or images from Web-Cam, the image is cropped into an oval or eclipse shape. Then the image is transformed into greyscale color and is normalized in order to reduce color complexities. We also focus on the special characteristics of human facial aspects such as nostril areas and oral areas. After every essential aspectsarescrutinized, the input image goes through the recognition system for facial identification. In some cases where the input image from the Web-Cam does not exist in the database, the user will be notified for the error handled. However, in cases where the image exists in the database, that image will be computed for similarity measurement using Euclidean Distance measure from the input image. Results and Conclusion: The result of our experiment reveals that the recognition process of 150 images in the database and 10 images from the Web-Cam provides 100% accuracy in terms of recognition. The runtime in this case is at 0.04 sec.
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- Successes Mean Quantization Transform (SMQT)
- Neural Network (NN)
- Histogram Equalization (HE)
- Local Binary Patterns (LBP)