Face Detection using Ferns

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

An Alternative Scanning Strategy to Detect Faces

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

Alternative Search Techniques for Face Detection Using Location Estimation and Binary Features

The sliding window approach is the most widely used technique to detect objects from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as grid spacing, and scale at which the image is searched). Scanning grid spacing controls the number of subwindows being processed, thus controlling the speed of detection. When the scanning grid spacing is larger than the tolerance of the trained classifier it can suffer from low detections. In this thesis, we propose an alternative search technique, which can improve the detections when lesser number of subwindows are processed. First, we present a technique to reduce the number of miss detections while increasing the grid spacing when using the sliding window approach for object detection. This is achieved by using a small patch to predict the location of an object within a local search area. To achieve speed, it is necessary that the time taken for location prediction is comparable or better than the time it takes in average for the object classifier to reject a subwindow. We use binary features and a decision tree as it proved to be efficient for our application. In the process we also propose a variation of an existing binary feature (Ferns) with similar performance, and requires only half the number of pixel access when compared to Fern feature. We analyze the effect of patch size on location estimation and also evaluate our approach on several face databases. Experimental evaluation shows better detection rate and speed with our proposed approach for larger grid spacing (lesser number of subwindows) when compared to standard scanning technique. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can achieve better performance even when fewer number of subwindows are processed. The interest points detected can be assumed as a non-regular grid compared to regular grid in the sliding window framework. A few image patches are sampled around an interest point for estimating the probable face location and further verified using a strong face classifier. Experiment results show that using an interest point detector can reduce the number of subwindows processed while maintaining a good detection rate

Alternative search techniques for face detection using location estimation and binary features

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as the grid spacing, and the scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper, we present a technique to reduce the number of missed detections when fewer subwindows are processed in the sliding window approach for face detection. This is achieved by using a small patch to predict the location of the face within a local search area. We use simple binary features and a decision tree for location estimation as it proved to be efficient for our application. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can further improve the performance. Experimental evaluation on several face databases show better detection rate and speed with our proposed approach when fewer number of subwindows are processed compared to the standard scanning technique. (C) 2013 Published by Elsevier Inc

Fast Bounding Box Estimation based Face Detection

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as cascade), the scanning speed also depends on number of different factors (such as grid spacing, and scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper we present a technique to reduce the number of miss detections while increasing the grid spacing when using the sliding window approach for object detection. This is achieved by using a small patch to predict the bounding box of an object within a local search area. To achieve speed it is necessary that the bounding box prediction is comparable or better than the time it takes in average for the object classifier to reject a subwindow. We use simple features and a decision tree as it proved to be efficient for our application. We analyze the effect of patch size on bounding box estimation and also evaluate our approach on benchmark face database. Since perturbing the training data can have an affect on the final performance, we evaluate our approach for classifiers trained with and without perturbations and also compare with OpenCV. Experimental evaluation shows better detection rate and speed with our proposed approach for larger grid spacing when compared to standard scanning technique