Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people’s identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (FastICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.     

A comprehensive study on face recognition: methods and challenges

ABSTRACT

Face Recognition is the process of identifying and verifying the faces. Face recognition has vast importance in the field of Security, Healthcare, Banking, Criminal Identification, Payment, and Advertising. In this paper, we have reviewed various techniques and challenges for the face recognition. Illumination, pose variation, facial expressions, occlusions, aging, etc. are the key challenges to the success of face recognition. Pre-processing, Face Detection, Feature Extraction, Optimal Feature Selection, and Classification are primary steps in any face recognition system. This paper provides a detailed review of each. Feature extraction techniques can be classified as appearance-based methods or geometry-based methods, such method may be local or global. Feature extraction is the most crucial stage for the success of the face recognition system. However, deep learning methods have freed the user from handcrafting the features. In this article, we have surveyed state-of-the-art methods of last few decades and the comparative study of various feature extraction methods is provided. Article also describes the current challenges in the area.     

Face recognition using Krawtchouk moment

Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face recognition system, which has the ability to extract local features from any region of interest. Krawtchouk moment is used to extract both local features and global features of the face. The extracted features are fused using summed normalized distance strategy. Nearest neighbour classifier is employed to classify the faces. The proposed method is tested using ORL and Yale databases. Experimental results show that the proposed method is able to recognize images correctly, even if the images are corrupted with noise and possess change in facial expression and tilt.     

A broad review about face recognition – feature extraction and recognition techniques

Abstract

Face recognition (FR) throws open a vast horizon of challenging tasks in the arena of facial image processing applications and computer visualisation, and hence has riveted keen interest during the last few years on account of its versatile applications in numerous spheres. Creating a useful facial design from initial face images is a very important gradient for victorious facial expression detection. Here, we furnish a report of several feature extraction and recognition methods which find themselves employed in the method of FR. The major aim of this survey is to assess the diverse FR methods according to their feature extraction and recognition techniques. From the analysis, we come to know about the feature extraction and recognition methods which have been elegant utilised in the FR procedure. They also vividly establish the technique which has performed excellently yielding superior FR precision by detecting face images more exactly. Moreover our study draws a concise picture of the feature extraction and recognition techniques and acts as a lodestar to the incoming intriguing investigators intending to increase their information about this innovative technique.     

Emotion recognition based on facial components

Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human–computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from these expressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques.     

基于深度数据的空间人脸旋转角度估计

提出一种基于三维人脸深度数据的人脸姿态计算方法。利用人脸的深度数据以及与其一一对应的灰度图像,根据微分几何原理和相应的曲率算法与人脸数据中的灰度特征对人脸面部关键特征点定位,进而计算出人脸姿态在三维空间中的3个姿态角。实验证明该方法能在姿态变化情况下实现对人脸旋转角的准确估计,为进一步的人脸识别和表情分析提供基础。     

非均衡训练集过采样的印刷套准识别方法

目的 针对印刷标志图像训练数据集非均衡性导致印刷标志图像中少类数据套准状态识别准确率低的问题,提出改进的SMOTE训练集过采样方法,以提高少类数据的识别准确率。方法 提取印刷标志图像灰度行程矩阵的纹理特征,组成多维的模型输入特征数据。基于少类样本的邻域信息,得到少类样本的过采样参数。对少类样本采取不同的过采样策略,实现训练集样本的均衡。使用均衡的训练集建立支持向量机模型,实现对印刷套准状态的识别。结果 实验结果表明,文中方法在不同非均衡印刷数据集上,获得的平均分类准确率几何平均数Gmean为0.8507,召回率Re为0.7192,ROC曲线下面积A为0.8549。结论 文中方法在不同非均衡印刷套准数据集上的分类性能要优于实验中的SMOTE,IS和SVM等方法。     

Virtual images inspired consolidate collaborative representation-based classification method for face recognition

Abstract

The collaborative representation-based classification method performs well in the field of classification of high-dimensional images such as face recognition. It utilizes training samples from all classes to represent a test sample and assigns a class label to the test sample using the representation residuals. However, this method still suffers from the problem that limited number of training sample influences the classification accuracy when applied to image classification. In this paper, we propose a modified collaborative representation-based classification method (MCRC), which exploits novel virtual images and can obtain high classification accuracy. The procedure to produce virtual images is very simple but the use of them can bring surprising performance improvement. The virtual images can sufficiently denote the features of original face images in some case. Extensive experimental results doubtlessly demonstrate that the proposed method can effectively improve the classification accuracy. This is mainly attributed to the integration of the collaborative representation and the proposed feature-information dominated virtual images.     

不均衡训练集特征选择的印刷套准状态识别

目的 针对不均训练集导致印刷套准识别模型无法较好识别印刷套不准图像的问题,提出基于最大相关、最小冗余的印刷标志图像数据特征选择方法.方法 提取印刷标志图像的多维特征数据,计算特征与印刷套准和印刷套不准2类之间的相关性和特征之间的冗余度.确定特征选择的目标函数,通过增量搜索方法寻找最优特征,加入特征子集,实现不均衡印刷标志图像的特征选择.结果 文中的特征选择方法获得了3项不均衡数据分类性能评价指标,A为0.9900,R为0.9400,Gmean为0.9466.结论 在不均衡印刷标志图像套准识别中,文中提出的方法性能优于实验中的未处理方法、PCA方法、Relief方法和NCA方法.     

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

In this article, attention-based mechanism with the enhancement on biologically inspired network for emotion recognition is proposed. Existing bio-inspired models use multiscale and multiorientation architecture to gain discriminative power and to extract meticulous visual features. Prevailing HMAX model represents S2 layers by randomly selected prototype patches from training samples that increase the computational complexity and degrade the discerning ability. As eyes and mouth regions are the most powerful and reliable cues in determining facial emotions, they serve as the prototype patches for S2 layer in HMAX model. Audio code 4 book is constructed from mel-frequency cepstral coefficients, temporal and spectral features processed by principal component analysis. Audio and video data features are fused to train support vector machine classifier. The attained results on eNTERFACE, surrey audio-visual expressed emotion and acted facial expressions in the wild database datasets ascertain the efficiency of the proposed architecture for emotion recognition.     

Research on Facial Expression Capture Based on Two-Stage Neural Network

To generate realistic three-dimensional animation of virtual character, capturing real facial expression is the primary task. Due to diverse facial expressions and complex background, facial landmarks recognized by existing strategies have the problem of deviations and low accuracy. Therefore, a method for facial expression capture based on two-stage neural network is proposed in this paper which takes advantage of improved multi-task cascaded convolutional networks (MTCNN) and high-resolution network. Firstly, the convolution operation of traditional MTCNN is improved. The face information in the input image is quickly filtered by feature fusion in the first stage and Octave Convolution instead of the original ones is introduced into in the second stage to enhance the feature extraction ability of the network, which further rejects a large number of false candidates. The model outputs more accurate facial candidate windows for better landmarks recognition and locates the faces. Then the images cropped after face detection are input into high-resolution network. Multi-scale feature fusion is realized by parallel connection of multi-resolution streams, and rich high-resolution heatmaps of facial landmarks are obtained. Finally, the changes of facial landmarks recognized are tracked in real-time. The expression parameters are extracted and transmitted to Unity3D engine to drive the virtual character's face, which can realize facial expression synchronous animation. Extensive experimental results obtained on the WFLW database demonstrate the superiority of the proposed method in terms of accuracy and robustness, especially for diverse expressions and complex background. The method can accurately capture facial expression and generate three-dimensional animation effects, making online entertainment and social interaction more immersive in shared virtual space.     

Improving representation-based classification for robust face recognition

The sparse representation classification (SRC) method proposed by Wright et al. is considered as the breakthrough of face recognition because of its good performance. Nevertheless it still cannot perfectly address the face recognition problem. The main reason for this is that variation of poses, facial expressions, and illuminations of the facial image can be rather severe and the number of available facial images are fewer than the dimensions of the facial image, so a certain linear combination of all the training samples is not able to fully represent the test sample. In this study, we proposed a novel framework to improve the representation-based classification (RBC). The framework first ran the sparse representation algorithm and determined the unavoidable deviation between the test sample and optimal linear combination of all the training samples in order to represent it. It then exploited the deviation and all the training samples to resolve the linear combination coefficients. Finally, the classification rule, the training samples, and the renewed linear combination coefficients were used to classify the test sample. Generally, the proposed framework can work for most RBC methods. From the viewpoint of regression analysis, the proposed framework has a solid theoretical soundness. Because it can, to an extent, identify the bias effect of the RBC method, it enables RBC to obtain more robust face recognition results. The experimental results on a variety of face databases demonstrated that the proposed framework can improve the collaborative representation classification, SRC, and improve the nearest neighbor classifier.     

Occlusion invariant face recognition using mean based weight matrix and support vector machine

In this paper, a novel occlusion invariant face recognition algorithm based on Mean based weight matrix (MBWM) technique is proposed. The proposed algorithm is composed of two phases—the occlusion detection phase and the MBWM based face recognition phase. A feature based approach is used to effectively detect partial occlusions for a given input face image. The input face image is first divided into a finite number of disjointed local patches, and features are extracted for each patch, and the occlusion present is detected. Features obtained from the corresponding occlusion-free patches of training images are used for face image recognition. The SVM classifier is used for occlusion detection for each patch. In the recognition phase, the MBWM bases of occlusion-free image patches are used for face recognition. Euclidean nearest neighbour rule is applied for the matching. GTAV face database that includes many occluded face images by sunglasses and hand are used for the experiment. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the MBWM based method shows superior performance to other conventional approaches.     

Face recognition system based on MOTIF features

This work addresses the use of the MOTIF algorithm for face feature extraction. The MOTIF algorithm is commonly used to characterize texture and shows good performance in this task; a MOTIF algorithm without the Co-occurrence Matrix is proposed to obtain face features, and the approach proves to be effective. System testing was based on a standard database (the AR Face database) that includes 120 people, 70 images with face expressions and 30 with sunglasses; 1 to 9 images were used to make the template for each person. After using Euclidean distance, Cosine distance and support vector machine as classifiers, correct classification was achieved with 98% accuracy. Further tests were performed with all databases and compared with Local Binary Pattern, DI-WBP and other commonly used schemes, demonstrating effective face recognition by the MOTIF algorithm without the co-occurrence matrix in addition to its fast performance due to the low computational cost.     

Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging

A robust smile recognition system could be widely used for many real-world applications. Classification of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face images. In this paper, an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade classifiers. Our model takes advantage of the intrinsic association between face detection, smile, and other face features to alleviate the over-fitting issue on the limited training set and increase classification results. The features are extracted taking into account to exclude any unnecessary coefficients in the feature vector; thereby enhancing the discriminatory capacity of the extracted features and reducing the computational process. Still, the main causes of error in learning are due to noise, bias, and variance. Ensemble helps to minimize these factors. Combinations of multiple classifiers decrease variance, especially in the case of unstable classifiers, and may produce a more reliable classification than a single classifier. However, a shortcoming of bagging as the best ensemble classifier is its random selection, where the classification performance relies on the chance to pick an appropriate subset of training items. The suggested model employs a modified form of bagging while creating training sets to deal with this challenge (error-based bootstrapping). The experimental results for smile classification on the JAFFE, CK+, and CK+48 benchmark datasets show the feasibility of our proposed model.     

基于SVD-TRIM特征和LSSVM人脸识别方法

人脸特征的选择对识别结果起关键作用。传统上只提取较大奇异值特征作为识别特征的人脸识别方法,识别率不高,对表情和姿态变化敏感。SVD-TRIM算法选择的奇异值识别特征融合了人脸整体和局部细节特征,并采用基于＂一对一＂的LSSVM多类分类器分类识别。实验结果表明SVD-TRIM算法选择的识别特征对提高识别率具有较大贡献,且对光照、姿态和表情具有鲁棒性。     

利用人眼分形码距离进行快速人脸识别

在利用人脸分形码距离进行识别时,需要大量的时间对人脸库中每张人脸图像进行迭代与距离运算.为克服这一缺点,本文提出了用水平方向高频子带来定位眼睛并将其从人脸中抽取出来,进一步提出了基于人眼分形码距离的人脸快速识别算法.利用该算法,可去掉大部分人眼分形码距离较大的图像,从识别时间复杂性分析,本文算法所需时间主要与人眼大小以及用于最后识别的图像数目有关.在ORL和YALE两个人脸库上的实验结果表明,与本征脸方法和直接利用人脸分形码距离方法比较,在用于最后识别的图像数目占人脸库中人脸总数的20%左右时,本文算法可使平均识别率保持在约90%,与其它方法基本持平.