Real Time Multiple Face Recognition
| Autor: | Shobhit Mittal, Madhav J. Nigam, Shubham Agarwal |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Biometrics
business.industry Computer science Dimensionality reduction Deep learning Feature extraction Pattern recognition 02 engineering and technology Facial recognition system Fingerprint Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Face detection |
| Zdroj: | Proceedings of the 2018 International Conference on Digital Medicine and Image Processing. |
| DOI: | 10.1145/3299852.3299853 |
| Popis: | Though a lot of research has already been done in the field of Face Recognition, one amongst the remaining challenges is recognizing multiple faces in weird conditions in a large group size. A robust face recognition system has been developed which detects faces in multiple, occluded, posed images obtained under low illumination conditions. The detector is a trained 34 layered Residual Network which obtains an accuracy of 98.4% on Visual Geometry Group Dataset [1]. A hybrid model has been proposed by combining the Residual Network detector with the novel approach of face embedding using triplet loss function [2] for recognition. The numerical and graphical results attached in the report depict the effectiveness of the proposed model for a variety of conditions. A 22 layered Inception Network has been trained for feature extraction and it achieves an accuracy of 99.5% on Labeled Faces in the Wild Dataset [3]. To achieve a similar accuracy on real life scenarios different methods like dimensionality reduction and data augmentation have been implemented. A mobile application has also been developed which utilizes the above described hybrid model for identification of people present in a large group. This application outweighs the fingerprint biometric in terms of speed, cost and group size. |
| Databáze: | OpenAIRE |
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