Fractional Krill–Lion Algorithm Based Actor Critic Neural Network for Face Recognition in Real Time Surveillance Videos
| Autor: | Deepak S. Dharrao, Nilesh J. Uke |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Krill
biology Artificial neural network Computer science Speech recognition media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology biology.organism_classification Facial recognition system Computer Science Applications Theoretical Computer Science Viola jones algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Software media_common |
| Zdroj: | International Journal of Computational Intelligence and Applications. 18 |
| ISSN: | 1757-5885 1469-0268 |
| Popis: | Face recognition from low quality videos is one of the major challenges prevailing in video surveillance system. Several works have been contributed towards the face recognition, but have suffered due to the fact that the low quality videos have face part with low resolution. Also, using traditional feature extraction schemes make the recognition process to be tough. This paper introduces a novel feature descriptor and classification scheme for recognizing the face from low quality videos. Here, the video frames are sequentially provided to the Viola–Jones algorithm for detecting the face part, and the quality of the face part is improved by applying bi-cubic interpolation based super resolution scheme. Now, the features of enhanced video frame are extracted using proposed local direction feature descriptor, namely scattering wavelet-based local directional pattern (SW-LDP). Then, the extracted features are fed as the input to the actor critic neural network, where training is done using the newly developed fractional calculus based krill–lion (fractional KL) algorithm. The proposed fractional KL-ACNN algorithm is experimented using the standard FAMED database. From the analysis, it is evident that the proposed classifier achieved low FAR of 3.89%, and low FRR of 4.04%, and high accuracy value of 95%, respectively. |
| Databáze: | OpenAIRE |
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