#5840. Multiscale parallel deep CNN (mpdCNN) architecture for the real low-resolution face recognition for surveillance

July 2026publication date
Proposal available till 17-05-2025
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Journal’s subject area:
Computer Vision and Pattern Recognition;
Signal Processing;
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Abstract:
Images from the surveillance networks are being extensively used for the purpose of monitoring and criminal investigations. However, it is often difficult to recognize faces using the data from the surveillance networks because the resolution of the face in the images captured is too low. Further, the low-resolution images have varying magnitude of facial feature content because of wide variations in illumination, pose, resolution and the distance from which the image is captured. Also, a single face recognition solution is not able to recognize faces efficiently in both high and low-resolution images. Wide variations in facial feature content in high and low-resolution images causes difficulty in classification of the features by a single model for the purpose of face recognition. We present a Deep-CNN based architecture called mpdCNN to solve the problem of face recognition in low as well as high-resolution images with high accuracy and robustness.
Keywords:
Convolution neural network; Face recognition; Low-resolution images; Multiscale parallel architecture; Surveillance

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