Face image quality assessment for face selection in surveillance video using convolutional neural networks

S, Vignesh and K V S N L, Manasa Priya and Channappayya, Sumohana (2015) Face image quality assessment for face selection in surveillance video using convolutional neural networks. In: Global Conference on Signal and Information Processing (GlobalSIP), 14-16 Dec, 2015, Orlando, FL.

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Automated Face Quality Assessment (FQA) plays a key role in improving face recognition accuracy and increasing computational efficiency. In the context of video, it is very common to acquire multiple face images of a single person. If one were to use all the acquired face images for the recognition task, the computational load for Face Recognition (FR) increases while recognition accuracy decreases due to outliers. This impediment necessitates a strategy to optimally choose the good quality face images from the pool of images in order to improve the performance of the FR algorithm. Toward this end, we propose a FQA algorithm that is based on mimicking the recognition capability of a given FR algorithm using a Convolutional Neural Network (CNN). In this way, we select those face images that are of high quality with respect to the FR algorithm. The proposed algorithm is simple and can be used in conjunction with any FR algorithm. Preliminary results demonstrate that the proposed method is on par with the state-of-the-art FQA methods in improving the performance of FR algorithms in a surveillance scenario.

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IITH Creators:
IITH CreatorsORCiD
Channappayya, SumohanaUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: face recognition, neural nets, video surveillance
Subjects: Others > Electricity
Divisions: Department of Electrical Engineering
Depositing User: Library Staff
Date Deposited: 13 May 2016 04:23
Last Modified: 01 Sep 2017 06:20
URI: http://raiith.iith.ac.in/id/eprint/2351
Publisher URL: https://doi.org/10.1109/GlobalSIP.2015.7418261
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