This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

http://www.eecs.qmul.ac.uk/~sgg/papers/ShanGongMcOwan_IVC09.pdf

Facial expression recognition based on Local Binary Patterns: A comprehensive study

Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.

/pdf/age-and-gender-classification-using-convolutional-neural-4qrrkmmyyh.pdf

Age and gender classification using convolutional neural networks

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

/pdf/local-binary-patterns-and-its-application-to-facial-image-1kaldi7ot7.pdf

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and support vector machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available.

/pdf/robust-facial-expression-recognition-using-local-binary-1tsdxnis6s.pdf

Robust facial expression recognition using local binary patterns

https://www.cin.ufpe.br/~cabm/visao/artigos/Learning%20local%20binary%20patterns%20for%20gender%20classification%20on%20real-world%20face%20images.pdf

Caifeng Shan

Papers

Facial expression recognition based on Local Binary Patterns: A comprehensive study

Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Robust facial expression recognition using local binary patterns

Learning local binary patterns for gender classification on real-world face images

Real-time hand tracking using a mean shift embedded particle filter