Machine vision quality assessment for robust face detection

Soundararajan, Rajiv and Biswas, Soma (2019) Machine vision quality assessment for robust face detection. In: SIGNAL PROCESSING-IMAGE COMMUNICATION, 72 . pp. 92-104.

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Abstract

Machine vision quality (MVQ) assessment for face detection refers to image quality as judged by face detection algorithms. While perceptual image quality usually depends on human observers, MVQ depends on factors such as the face detection algorithm and performance measures such as recall and precision. We define the MVQ index as a weighted combination of the predicted precision and recall of the face detection algorithm and predict it using image features based on natural scene statistics. A filter bank framework is developed to achieve image enhancement of distorted images for face detection, where the enhancement operations can be optimized for either perceptual quality or MVQ. It is shown that MVQ can be effectively used to control the relative performance of recall and precision that can be achieved on enhanced images. For certain face detection algorithms, optimizing for MVQ instead of perceptual quality can lead to improved face detection performance. The MVQ is developed for three different face detection algorithms and the image enhancement framework is tested on a dataset based on the IDEAL-LIVE Distorted Face Database. A computationally efficient method to optimize for MVQ is designed by predicting the MVQ of enhanced images directly from features extracted from distorted images and filter parameters. The optimization framework is further enhanced by allowing for filter bank independent optimization of MVQ.

Item Type:	Journal Article
Publication:	SIGNAL PROCESSING-IMAGE COMMUNICATION
Publisher:	ELSEVIER SCIENCE BV
Additional Information:	Copyright for this article belongs to Elsevier.
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering Division of Electrical Sciences > Electrical Engineering
Date Deposited:	18 Mar 2019 10:00
Last Modified:	18 Mar 2019 10:00
URI:	http://eprints.iisc.ac.in/id/eprint/61927

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