Application of Complete Local Binary Pattern Method for facial expression recognition

Facial expression recognition is a rapidly growing area of research in computer vision due to its immense applications such as human-computer interaction, psychology and intelligent robotic systems etc. Various methods have been proposed in past two decades aiming to solve different issues in this aspect. Most of the researches used distinct databases for the evaluation of their techniques, so it is very challenging task to infer which one is superior. In this paper, we have implemented three methods of facial expression recognition named: Principal Component Analysis (PCA), Artificial Neural Network (ANN) and Local Binary Pattern (LBP) and evaluated their performances using JAFFE database. Keywords: Facial Expression Recognition, Emotion Recognition, JAFFE Dataset, PCA, LBP, ANN.

With the work and study pressure on people increasing and the importance of mental health problems on the rise, facial expression analysis plays an important role in mental health auxiliary diagnosis and treatment (MHADT). This study proposes a facial expression localization and recognition model that integrates Main Directional Mean Optical Flow (MDMO) with the Transformer architecture. It addresses the problems of insufficient generalization ability and limited temporal modeling ability of traditional methods in facial expression recognition. The study is based on the authoritative Audio/Visual Emotion Challenge 2019 Depression Detection Sub-challenge (AVEC2019 DDS) dataset in the mental health field, which contains 163 training samples, 56 validation samples, and 56 test samples. With Explicit Shape Regression, Local Binary Patterns, Mnemonic Descent Method, Convolutional Neural Network, and benchmark models as comparison objects, it systematically evaluates the model's performance in error, accuracy, and processing speed. The results show that the model achieves the best performance in both facial expression localization and recognition tasks. The validation set errors are 7.26 and 6.85, the localization accuracy reaches 89.6%, and the recognition accuracy reaches 88.9%, which is significantly better than other methods. At the same time, its image processing time is 55 milliseconds (ms) and 44ms, balancing high precision and real-time performance. The study indicates that the fusion of MDMO and Transformer can effectively capture the spatial and temporal features of facial expressions. Thus, this fusion method provides an efficient, stable, and scalable technical solution for emotion recognition in MHADT. This improves both the FER and localization accuracy and effect. Besides, it provides a novel reference in both approaches and application level for the intelligent development of mental health evaluation.

This paper presents a new method for facial expression recognition using divided local binary pattern features. Motivated by Fourier’s parity decomposition, we decompose a LBP descriptor into two subdescriptors. This method reduces the amount of data of the feature extraction, reducing the data dimension effectively. Experimental results on JAFFE facial expression database show that the proposed method can get significant performance improvement in recognition rate, reduce the recognition time greatly.

A Facial Expression Recognition (FER) method based on Conv3D-ConvLSTM-SEnet in an online education environment is proposed to address the issue of low accuracy in current classroom expression recognition (FER) methods. Firstly, ConvLSTM is used to integrate the local feature extraction ability of CNN and the temporal modeling ability of LSTM, and based on this, the rich spatial features of the image are characterized. Then, by introducing Depth Separated Convolution (DSC) to change the number of output channels and separate each channel, the Feature Maps (F-M) are sequentially concatenated to obtain a multi-channel output F-M. Finally, based on ConvLSTM and SEnet modules, a Conv3D ConvLSTM-based FER model was proposed by redistributing the extracted abstract features and basic texture features, achieving high accuracy classroom FER. The proposed classroom FER method and the other five methods were compared and analyzed through simulation experiments using the CK[Formula: see text], FER2013 and JAFFE datasets, respectively. The results indicate that the proposed method achieves the highest accuracy, precision, recall, and F1 score, with improvements of at least 1.85%, 2.41%, 1.18% and 2.05%, respectively, compared to the other five methods on the FER2013 dataset. The proposed method can perform FER on students in online classroom teaching, detect their emotions in real-time, and help teachers make better course teaching adjustments based on this.

Facial expression recognition is a practical application in the field of artificial intelligence. This article focuses on the introduction of the principles of each facial expression recognition method based on deep learning in chronological order, as well as the advantages and disadvantages of each method. The methods of facial expression recognition based on deep learning introduced in this article are as follows: Li Dunyu [1] proposed a method of LBP and BP neural network to realize facial expression recognition. And then the convolutional neural network is widely used, Zhou Benjun [2] merged LBP and convolutional neural network to realize facial expression recognition; Liu [3] establish a model that combines a recurrent neural network with a 3D convolutional neural network, and added a timeline so that the machine can extract and recognize facial expressions in a video; Jung [4] proposed to combine the convolutional neural network and the deep neural network to form a new neural network DTAGN, by using this new network he completes facial expression recognition; Shi Xugan [5] proposed a model of combining deep belief network with multi-layer perceptron (MLP), and successfully applied it to the research of facial expression recognition. By utilizing joint training of feature points and multiple networks Xia Tian [6] finish the research; Zhang Feifei [7] and other scholars proposed multi-pose facial expression recognition based on generating an adversarial network.

This paper presents an analysis of hand-crafted and learned feature extraction methods for real-time facial expression recognition (FER). Our analysis focuses on methods capable of running on mobile devices, including traditional algorithms such as Gabor transform, HOG, LBP, as well as two compact CNN models, named Mobilenet V1 and V2. Additionally, we test the performance of MOTIF, a highly efficient texture feature extractor algorithm. Furthermore, we analyze the contribution of the mouth and front-eyes regions for recognizing the seven basic facial expressions. Experimental results are evaluated on two publicly available datasets. KDEF database which was captured under controlled conditions and RAF database which represents more naturalistic expressions captured in-the-wild. Under the same experimental conditions, MOTIF presents the fastest performance by sacrificing accuracy, while Mobilenet V2 presents the highest results with considerable speed and model size.

Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier

With the continuous development of facial expression technology, especially the development of deep learning, and the establishment of in-the-wild datesets in recent years, field face recognition has become a hot research field in the wild of facial expression recognition. Different from the traditional facial expression recognition(FER), in-the-wild facial facial expression recognition, the problem of recognition accuracy caused by illumination, occlusion or low image resolution. Therefore, in order to solve these problems, new methods have been put forward continuously in recent years. In this paper, we first continue to summarize the widely used datesets, and then summarize the paper methods of facial expression recognition in the field proposed in the past two years.

In this paper, we propose a novel method for facial expression recognition. The facial expression is extracted from human faces by an expression classifier that is learned from boosting Haar feature based look-up-table type weak classifiers. The expression recognition system consists of three modules, face detection, facial feature landmark extraction and facial expression recognition. The implemented system can automatically recognize seven expressions in real time that include anger, disgust, fear, happiness, neutral, sadness and surprise. Experimental results are reported to show its potential applications in human computer interaction.

Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in the field of facial expression recognition (FER). In this paper, we present a comprehensive study on recently proposed state-of-the-art semi-supervised learning methods in the context of FER. We conduct comparative study on eight semi-supervised learning methods, namely Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various amounts of labeled samples are used. We also compare the performance of these methods against fully-supervised training. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets. To facilitate further research in this area, we make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER.

In order to solve the problems of low average gradient and long recognition time in traditional facial expression recognition method, a multi-scale detail enhancement method for facial dynamic expression recognition of sprint athletes is proposed. A principal component analysis method was used to establish the facial expression feature subspace of sprinters, to project and reduce the dimension of the facial dynamic expression feature vector of sprinters, and to obtain the low frequency information and high frequency information of the facial image of sprinters by bilateral filtering. The multi-scale details of expression are enhanced by using side suppression network model and improving image S curve. The feature vector of facial dynamic expression is input into support vector machine to recognise the facial dynamic expression of sprinter. Experimental results show that the average value of annoying gradient is about 98 and the shortest time s. is about 1.9.

Facial expressions recognition plays important role in human communication. It has become one of the most challenging tasks in the pattern recognition field. It has many applications such as: human computer interaction, video surveillance, forensic applications, criminal investigations, and in many other fields. In this paper we propose a method for facial expression recognition (FER). This method provides new insights into two issues in FER: feature extraction and robustness. For feature extraction we are using sparse representation approach after applying multiple Gabor filter and then using support vector machine (SVM) as classifier. We conduct extensive experiments on standard facial expressions database to verify the performance of proposed method. And we compare the result with other approach.

Over the last two decades automatic facial expression recognition has become an active research area. Facial expressions are an important channel of non-verbal communication, and can provide cues to emotions and intentions. This paper introduces a novel method for facial expression recognition, by assembling contour fragments as discriminatory classifiers and boosting them to form a strong accurate classifier. Detection is fast as features are evaluated using an efficient lookup to a chamfer image, which weights the response of the feature. An Ensemble classification technique is presented using a voting scheme based on classifiers responses. The results of this research are a 6-class classifier (6 basic expressions of anger, joy, sadness, surprise, disgust and fear) which demonstrate competitive results achieving rates as high as 96% for some expressions. As classifiers are extremely fast to compute the approach operates at well above frame rate. We also demonstrate how a dedicated classifier can be consrtucted to give optimal automatic parameter selection of the detector, allowing real time operation on unconstrained video.

This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes and mouth are extracted. In the classification of expression, using MCE criteria is adopted to improve the performance of HMM. The results show that the performance of expression recognition system can be improved to some extent by using Zernike moments and MCE criterion.

최근 얼굴 표정 인식에 있어 영상 기반의 방법의 하나로서 ULBP 블록 히스토그램 피쳐와 SVM을 분류기로 사용한 연구가 수행되었다. Ojala 등에 의해 소개된 LBP는 높은 식별력과 조명의 변화에 대한 내구성과 간단한 연산 때문에 영상 인식 분야에 많이 사용되고 있다. 본 논문에서는 ULBP 블록 히스토그램을 계산함에 있어 분할 영역의 이동, 크기 변화에 더하여 미세한 특징 요소를 표현할 수 있도록 <TEX>$LBP_{8,2}$</TEX>과 <TEX>$LBP_{8,1}$</TEX>를 결합하였다. <TEX>$LBP_{8,1}$</TEX> 660개, <TEX>$LBP_{8,2}$</TEX> 550개의 분할 창으로부터 1210개의 ULBP 히스토그램 피쳐를 추출하고 이로부터 AdaBoost를 이용하여 50개의 약 분류기를 생성하였다. <TEX>$LBP_{8,1}$</TEX>와 <TEX>$LBP_{8,2}$</TEX>가 결합된 하이브리드 형태의 ULBP 블록 히스토그램 피쳐와 SVM 분류기를 이용함으로써 표정 인식률을 향상시킬 수 있었으며 다양한 실험을 통하여 이를 확인하였다. 본 논문에서 제안한 하이브리드 Boosted ULBP 히스토그램의 경우에 표정의 인식률이 96.3%로 가장 높은 결과를 보였으며 제안한 방법의 우수성을 확인하였다. Recently, as one of images based methods in facial expression recognition, the research which used ULBP block histogram feature and SVM classifier was performed. Due to the properties of LBP introduced by Ojala, such as highly distinction capability, durability to the illumination changes and simple operation, LBP is widely used in the field of image recognition. In this paper, we combined <TEX>$LBP_{8,2}$</TEX> and <TEX>$LBP_{8,1}$</TEX> to describe micro features in addition to shift, size change in calculating ULBP block histogram. From sub-windows of 660 of <TEX>$LBP_{8,1}$</TEX> and 550 of <TEX>$LBP_{8,2}$</TEX>, ULBP histogram feature of 1210 were extracted and weak classifiers of 50 were generated using AdaBoost. By using the combined <TEX>$LBP_{8,1}$</TEX> and <TEX>$LBP_{8,2}$</TEX> hybrid type of ULBP histogram feature and SVM classifier, facial expression recognition rate could be improved and it was confirmed through various experiments. Facial expression recognition rate of 96.3% by hybrid boosted ULBP block histogram showed the superiority of the proposed method.