A Face Recognition Technique using Local Binary Pattern Method
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TLDR
This paper evaluates facial representation predicated on statistical local features, Local Binary Patterns, for facial expression recognition, and finds that LBP features are effective and efficient for face expression recognition.Abstract:
3 Abstract: LBP is really a very powerful method to explain the texture and model of a digital image. Therefore it was ideal for feature extraction in face recognition systems. A face image is first split into small regions that LBP histograms are extracted and then concatenated in to a single feature vector. This vector forms an efficient representation of the face area and can be used to measure similarities between images. Automatic facial expression analysis is a fascinating and challenging problem, and impacts important applications in several areas such as human- computer interaction and data-driven animation. Deriving a facial representation from original face images is an essential step for successful facial expression recognition method. In this paper, we evaluate facial representation predicated on statistical local features, Local Binary Patterns, for facial expression recognition. Various machine learning methods are systematically examined on several databases. Broad experiments illustrate that LBP features are effective and efficient for facial expression recognition.read more
Citations
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Automated attendance system using face recognition
TL;DR: Project Report Submitted in partial fulfillment of the requirements for the degree of Bachelor of Computer Engineering.
Proceedings ArticleDOI
Facial expression recognition using enhanced local binary patterns
TL;DR: The main contribution of the paper is the feature selection applied, in which the high variance LBP pixels are selected to represent faces, and the recognition rates were improved significantly.
Journal Article
Face Recognition Based on PCA Algorithm
TL;DR: PCA is used for face recognition to improve the accuracy of recognition and show that the proposed method has a high recognition rate for the face images in the experiments.
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Implementation of deep-learning algorithm for obstacle detection and collision avoidance for robotic harvester
TL;DR: It is shown that the pruned model can be used for obstacle detection and collision avoidance in robotic harvesters and when the compaction ratio is less than 80%, a more efficient model with a slightly reduced accuracy in comparison to the original model was achieved.
Journal ArticleDOI
A Study on Facial Expression Recognition Using Local Binary Pattern
Shahreen Kasim,Rohayanti Hassan,Nur Hadiana Zaini,Asraful Syifaa’ Ahmad,Azizul Azhar Ramli,Rd. Rohmat Saedudin +5 more
TL;DR: The observation on results obtained in facial expressions recognition rate indicated the effectiveness of the proposed algorithm based on SVM-LBP features, and the performance of feature extraction and classification is evaluated based on the recognition accuracy.
References
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Book ChapterDOI
Face Recognition with Local Binary Patterns
TL;DR: A novel approach to face recognition which considers both shape and texture information to represent face images and the simplicity of the proposed method allows for very fast feature extraction.
Journal ArticleDOI
Facial expression recognition based on Local Binary Patterns: A comprehensive study
TL;DR: This paper empirically evaluates facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition, and observes that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in compressed low-resolution video sequences captured in real-world environments.
Proceedings ArticleDOI
Robust facial expression recognition using local binary patterns
TL;DR: A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images.