Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard d...

Extraction of invariant features is the core of Face RecognitionSystems (FRS). This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptor known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG), which the face image is filtered with the multiscale multiresolution Gabor filter bank to generate multiple Gabor magnitude images (GMIs), then the do...

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An automatic face recognition system based on multiple facial features is described in this paper. Each facial feature is represented by a Gabor-based complex vector and is localized by an automatic facial feature detection scheme. Two face recognition approaches, named Two-Layer Nearest Neighbor (TLNN) and Modular Nearest Feature Line (MNFL) respectively, are proposed. Both TLNN and MNFL are b...

In the domain of object recognition, the SIFT feature [1] is known to be a very successful local invariant feature. The performance of the recognition task using SIFT features is very robust and also can be done in real-time. This project present an approach that adopt the SIFT feature for the task of face detection. A feature database is created for the detection of generic face features and a...

An algorithm to automatically detect facial features from color images has been developed. First, face region is located using skin-color information. Then, the iris candidates are extracted from the intensity valleys created from the detected face. Next, the costs for each pair of iris candidates are computed to determine the real pair of irises. Mouth region and corners are detected using col...

We compare four local feature extraction techniques for the task of face verification, namely (ordered in terms of complexity): raw pixels, raw pixels with mean removal, 2D Discrete Cosine Transform (DCT) and local Principal Component Analysis (PCA). The comparison is performed in terms of discrimination ability and robustness to illumination changes. We also evaluate the effectiveness of sever...

The face recognition (FR) system plays a vital role in commercial & law enforcement applications. Image resolution is an important factor affecting face recognition performance. The performance of face recognition system degrades by low resolution of face images. To address this problem, a super resolution (SR) method was introduced by Hua Huang and Huiting He [7], which uses Canonical correlat...

In this work we investigate a novel approach to handle the challenges of face recognition, which includes rotation, scale, occlusion, illumination etc. Here, we have used thermal face images as those are capable to minimize the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in po...