Partial Face Detection using Regions with Convolutional Neural Networks

Although many methods have been developed for holistic face detection, detecting partial faces hasn't been a successful endeavor yet. Partial faces frequently appear in real world environments like in surveillance videos and are quite difficult to detect. Recently, CNNs have shown very promising results with object detection in PASCAL VOC challenges. We propose an approach to detect partial faces along with holistic faces (frontal and profile views) present in natural scenarios. In the proposed method, we use CNN for feature extraction and representation. The drawback of CNN being computationally expensive is dealt with Selective Search using Segmentation, which reduces the search space to a great extent. We used FDDB Benchmarking for evaluating our method and the results were near the best results of all recent methods in the face detection domain

Real-time face detection using edge orientation matching

In this paper we describe our ongoing work on real-time face detection in grey level images using edge orientation information. We will show that edge orientation is a powerful local image feature to model objects like faces for detection purposes. We will present a simple and efficient method for template matching and object modeling based solely on edge orientation information. We also show how to obtain an optimal face model in the edge orientation domain from a set of training images. Unlike many approaches that model the grey level appearance of the face our approach is computationally very fast. It takes less than 0.08 seconds on a Pentium II 500MHz for a 320x240 image to be processed using a multi-resolution search with six resolution levels. We demonstrate the capability of our detection method on an image database of 17000 images taken from more than 2900 different people. The variations in head size, lighting and background are considerable. The obtained detection rate is more than 93% on that database