Histogram of Oriented Directional Features for Robust Face Recognition (original) (raw)
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Face Identification using Histogram
International Journal of Scientific Research in Science, Engineering and Technology, 2020
Various experiments or methods can be used for face recognition and detection however two of the main contain an experiment that evaluates the impact of facial landmark localization in the face recognition performance and the second experiment evaluates the impact of extracting the HOG from a regular grid and at multiple scales. We observe the question of feature sets for robust visual object recognition. The Histogram of Oriented Gradients outperform other existing methods like edge and gradient based descriptors. We observe the influence of each stage of the computation on performance, concluding that fine-scale gradients, relatively coarse spatial binning, fine orientation binning and high- quality local contrast normalization in overlapping descriptor patches are all important for good results. Comparative experiments show that though HOG is simple feature descriptor, the proposed HOG feature achieves good results with much lower computational time.
Design, Implementation and Evaluation of an Algorithm for Face Recognition Based on Modified Local Directional Pattern, 2013
Face Recognition is one of the most researched fields of image processing and computer vision. Recent developed algorithms were restricted controlled by the environmental condition such as illumination, expression. Proposed Algorithm aims to implement face recognition based on the Modified Local Directional Pattern (LDP) in which the concept of Local Binary Pattern which is strong enough to select good features under real world environmental condition. In the modified LDP, the face area is divided into small regions. An LDP histogram is extracted for each smaller region. All the histograms of each and every small regions are concatenated into a single vector to represent the face efficiently. This combined histogram is the final analysis of each and every image and stores the features for each ad every image. The classification is performed by using KNN classification. The performance of the algorithm has been checked for the different databases – YaleDB, ORL and CMUPIE .
Face Recognition using Histogram
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Human face conveys more information about identification, expression, and emotions of a person. In today's world every individual in the society wants to be more secure from unauthorized authentication. In order to provide more security, "Facial Recognition" has come into the picture and lead a most challenging role of detecting the face with more accurate results without any false identities. To increase the efficiency of the face recognition, histogram based facial recognition is chosen, where a face region is fragmented into a number of regions and histogram values are extracted and they are linked together into a single vector. This vector is compared for the similarities between the facial images and provides a most efficient outcome. Technique used in face recognition system is histogram. The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy.
A Novel Approach for Face Recognition under Varying Illumination Conditions
International Journal of Intelligent Information Technologies, 2018
Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better ...
Robust & Accurate Face Recognition using Histograms
res publicatiion, 2011
A large number of face recognition algorithms have been developed from decades. Face recognition systems have been grabbing high attention from commercial market point of view as well as pattern recognition field. It also stands high in researchers community. Face recognition have been fast growing, challenging and interesting area in real-time applications. This face recognition system detects the faces in a picture taken by web-cam or a digital camera, and these face images are then checked with training image dataset based on descriptive features. In this paper, we use a histogram approach for human face detection. Since different faces contains different facial features, having the features which are unique. In this paper the vector machine is used for skin detection and face detection.
Face Recognition with Patterns of Oriented Edge Magnitudes
2010
This paper addresses the question of computationally inexpensive yet discriminative and robust feature sets for real-world face recognition. The proposed descriptor named Patterns of Oriented Edge Magnitudes (POEM) has desirable properties: POEM (1) is an oriented, spatial multi-resolution descriptor capturing rich information about the original image; (2) is a multi-scale self-similarity based structure that results in robustness to exterior variations; and (3) is of low complexity and is therefore practical for real-time applications. Briefly speaking, for every pixel, the POEM feature is built by applying a self-similarity based structure on oriented magnitudes, calculated by accumulating a local histogram of gradient orientations over all pixels of image cells, centered on the considered pixel. The robustness and discriminative power of the POEM descriptor is evaluated for face recognition on both constrained (FERET) and unconstrained (LFW) datasets. Experimental results show that our algorithm achieves better performance than the state-of-the-art representations. More impressively, the computational cost of extracting the POEM descriptor is so low that it runs around 20 times faster than just the first step of the methods based upon Gabor filters. Moreover, its data storage requirements are 13 and 27 times smaller than those of the LGBP (Local Gabor Binary Patterns) and HGPP (Histogram of Gabor Phase Patterns) descriptors respectively.