Face Detection using Ferns | EPFL Graph Search

This paper discusses the use of ferns (a set of binary features) for face detection. The binary feature used here is the sign of pixel intensity difference. Ferns were first introduced for keypoint recognition and showed good performance, and improving the speed of recognition. Keypoint recognition deals with classification of few hundred different classes, while face detection is a two-class problem with an unbalanced data. For keypoint recognition random pixel pairs proved to be good enough while we used conditional mutual information criteria to select a small subset of informative binary feature to build class conditional densities and a Naive Bayesian classifier is used for face and non-face classification. We compared our approach with boosted haar-like features, modified census transform (MCT,',','), and local binary pattern on a single stage classifier. Results shows that ferns when compared to haar-like features are robust to illumination changes and comparable to boosted MCT feature. Finally a cascade of classifiers was built and the performance on cropped face images and the localization results using Jesorsky measure are reported on XM2VTS and BANCA database.

A framework for objective evaluation of privacy filters in video surveillance

Touradj Ebrahimi

Extensive adoption of video surveillance, affecting many aspects of our daily lives, alarms the public about the increasing invasion into personal privacy. To address these concerns, many tools have been proposed for protection of personal privacy in image and video. However, little is understood regarding the effectiveness of such tools and especially their impact on the underlying surveillance tasks, leading to a tradeoff between the preservation of privacy offered by these tools and the intelligibility of activities under video surveillance. In this paper, we investigate this privacy-intelligibility tradeoff objectively by proposing an objective framework for evaluation of privacy filters. We apply the proposed framework on a use case where privacy of people is protected by obscuring faces, assuming an automated video surveillance system. We used several popular privacy protection filters, such as blurring, pixelization, and masking and applied them with varying strengths to people's faces from different public datasets of video surveillance footage. Accuracy of face detection algorithm was used as a measure of intelligibility (a face should be detected to perform a surveillance task), and accuracy of face recognition algorithm as a measure of privacy (a specific person should not be identified). Under these conditions, after application of an ideal privacy protection tool, an obfuscated face would be visible as a face but would not be correctly identified by the recognition algorithm. The experiments demonstrate that, in general, an increase in strength of privacy filters under consideration leads to an increase in privacy (i.e., reduction in recognition accuracy) and to a decrease in intelligibility (i.e., reduction in detection accuracy). Masking also shows to be the most favorable filter across all tested datasets.

Spie-Int Soc Optical Engineering2013

An Alternative Scanning Strategy to Detect Faces

Venkatesh Bala Subburaman, Sébastien Marcel

The sliding window approach is the most widely used technique to detect faces in an image. Usually a classifier is applied on a regular grid and to speed up the scanning, the grid spacing is increased, which increases the number of miss detections. In this paper we propose an alternative scanning method which minimizes the number of misses, while improving the speed of detection. To achieve this we use an additional classifier that predicts the bounding box of a face within a local search area. Then a face/non-face classifier is used to verify the presence or absence of a face. We propose a new combination of binary features which we term as u-Ferns for bounding box estimation, which performs comparable or better than former techniques. Experimental evaluation on benchmark database show that we can achieve 15-30% improvement in detection rate or speed when compared to the standard scanning technique.

2010

Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features

Sébastien Marcel, Zohreh Mostaani

Makeup is a simple and easy instrument that can alter the appearance of a person’s face, and hence, create a presentation attack on face recognition (FR) systems. These attacks, especially the ones mimicking ageing, are difficult to detect due to their close resemblance with genuine (non-makeup) appearances. Makeups can also degrade the performance of recognition systems and of various algorithms that use human face as an input. The detection of facial makeups is an effective prohibitory measure to minimize these problems. This work proposes a deep learning-based presentation attack detection (PAD) method to identify facial makeups. We propose the use of a convolutional neural network (CNN) to extract features that can distinguish between presentations with age-induced facial makeups (attacks), and those without makeup (bona-fide). These feature descriptors, based on shape and texture cues, are constructed from multiple intermediate layers of a CNN. We introduce a new dataset AIM (Age Induced Makeups) consisting of 200+ video presentations of old-age makeups and bona-fide, each. Our experiments indicate makeups in AIM result in 14% decrease in the median matching scores of a recent CNN-based FR system. We demonstrate accuracy of the proposed PAD method where 93% presentations in the AIM dataset are correctly classified. In additional testing, it also outperforms existing methods of detection of generic makeups. A simple score-level fusion, performed on the classification scores of shape- and texture-based features, can further improve the accuracy of the proposed makeup detector.

2019