Face Image Classification Research Articles

Facial Emotion Detection through Deep Covolutional Neural Networks

Our society has evolved to a threshold where use of machines to automate mundane tasks is constantly increasing in daily life. Providing machines with capability to develop perception from their environment can lead them to perform a great variety of tasks. Facial emotion detection is crucial sub-part of machine perception development. In this article we present a deep learning based approach for Facial emotion Detection. Our model uses a Convolutional Neural Network (CNN) to learn deep features for classification of facial images into one of 22 emotion (Basic 7 + Compound 15) categories considered in this study. We trained our CNN model with the images dataset from Martinez et al. Our Facial Emotion Detection model was developed using keras with theano backend and implemented on a GPU-powered testbed. Our model achieved 67.6% accuracy for basic emotions and 33% accuracy for compound emotions.

Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition

The ear recognition task is known as predicting whether two ear images belong to the same person or not. More recently, most ear recognition methods have started based on deep learning features that can achieve a good accuracy, but it requires more resources in the training phase and suffer from time-consuming computational complexity. On the other hand, descriptor features and metric learning play a vital role and also provide excellent performance in many computer vision applications, such as face recognition and image classification. Therefore, in this paper, we adopt the descriptor features and present a novel metric learning method that is efficient in matching real-time for ear recognition system. This method is formulated as a pairwise constrained optimization problem. In each training cycle, this method selects the nearest similar and dissimilar neighbors of each sample to construct the pairwise constraints and then solves the optimization problem by the iterated Bregman projections. Experiments are conducted on Annotated Web Ears (AWE) database, West Pommeranian University of Technology (WPUT), the University of Science and Technology Beijing II (USTB II), and Mathematical Analysis of Images (AMI) databases.. The results show that the proposed approach can achieve promising recognition rates in ear recognition, and its training process is much more efficient than the other competing metric learning methods.

The conceptual structure of face impressions

Humans seamlessly infer the expanse of personality traits from others' facial appearance. These facial impressions are highly intercorrelated within a structure known as "face trait space." Research has extensively documented the facial features that underlie face impressions, thus outlining a bottom-up fixed architecture of face impressions, which cannot account for important ways impressions vary across perceivers. Classic theory in impression formation emphasized that perceivers use their lay conceptual beliefs about how personality traits correlate to form initial trait impressions, for instance, where trustworthiness of a target may inform impressions of their intelligence to the extent one believes the two traits are related. This considered, we explore the possibility that this lay "conceptual trait space"-how perceivers believe personality traits correlate in others-plays a role in face impressions, tethering face impressions to one another, thus shaping face trait space. In study 1, we found that conceptual and face trait space explain considerable variance in each other. In study 2, we found that participants with stronger conceptual associations between two traits judged those traits more similarly in faces. Importantly, using a face image classification task, we found in study 3 that participants with stronger conceptual associations between two traits used more similar facial features to make those two face trait impressions. Together, these findings suggest lay beliefs of how personality traits correlate may underlie trait impressions, and thus face trait space. This implies face impressions are not only derived bottom up from facial features, but also shaped by our conceptual beliefs.

Sparsifying transform learning for face image classification

Sparse signal representation showed promising results in the field of face recognition in the past few years. An algorithm based on a sparsifying transform is considered. It mainly learns a dictionary that can transform the image into sparse vectors. In the transformation domain, the images of the same class should have similar non-zero coefficients pattern that can be used for identification. The classification process of this method only requires to transform the image and make norm comparisons to determine the class of the image. The proposed method shows a comparable performance with the other known methods in the literature by means of accuracy. A novel method in sparsity-based image identification that uses analysis dictionaries is proposed, unlike the conventional sparsity-based methods. One advantage of the proposed algorithm is the low computational cost of the classification process.

Face age classification based on a deep hybrid model

Face age estimation, a computer vision task facing numerous challenges due to its potential applications in identity authentication, human–computer interface, video retrieval and robot vision, has been attracting increasing attention. In recent years, the deep convolutional neural networks (DCNN) have achieved state-of-the-art performance in age classification of face images. We propose a deep hybrid framework for age classification by exploiting DCNN as the raw feature extractor along with several effective methods, including fine-tuning the DCNN into a fine-tuned deep age feature extraction (FDAFE) model, introducing a new method of feature extracting, applying the maximum joint probability classifier to age classification and a strategy to incorporate information from face images more effectively to improve estimation capabilities further. In addition, we pre-process the original image to represent age information more accurately. Based on the discriminative and compact framework, state-of-the-art performance on several face image data sets has been achieved in terms of classification accuracy.

An alternative to face image representation and classification

Sparse representation has brought a breakthrough to the face recognition community. It mainly attributes to the creative idea representing the probe face image by a linear combination of the gallery images. However, for face recognition applications, sparse representation still suffers from the following problem: because the face image varies with the illuminations, poses and facial expressions, the difference between the test sample and training samples from the same subject is usually large. Consequently, the representation on the probe face image provided by the original gallery images is not competent in accurately representing the probe face, which may lead to misclassification. In order to overcome this problem, we propose to modify training samples to produce an alternative set of the original training samples, and use both of the original set and produced set to obtain better representation on the test sample. The experimental results show that the proposed method can greatly improve previous sparse representation methods. It is notable that the error rate of classification of the proposed method can be 10% lower than previous sparse representation methods.

Face recognition based on Volterra kernels direct discriminant analysis and effective feature classification

We present a novel face recognition method based on direct discriminant Volterra kernels and effective feature classification (DD-VK). One of the crucial steps involves dividing face images into patches and using the DD-VK method to extract the features of sub-image patches. DD-VK implements diagonalization to discard useless information in the null space of the inter-class scatter matrix and preserve important discriminant information in the null space of the intra-class scatter matrix. This method can simultaneously maximize inter-class distances and minimize intra-class distances in the feature space. We also introduce a novel classification scheme associated with the 2D Volterra kernel feature. Our scheme aggregates the classification information obtained from each column of the feature matrix in each image patch and uses a voting strategy to implement parent face image classification. This procedure can reduce the influence of local negative information. Experimental results show that the proposed method demonstrates good performance when dealing with conventional face recognition problems and exhibits strong robustness when dealing with block occlusion images.

Pixel Based Supervised Classification of Hyperspectral Face Images for Face Recognition

Hyperspectral facial data presents creative information, taken with Hyperspectral camera, as compared to the traditional camera and images. This facial spectral dataset is a rich source of information extraction and analysis over traditional single band data with constant wavelength of Electromagnetic Spectrum (EMS). Herein study, Hyperspectral Image (HSI) classification and recognition have done in Visible to Near Infrared (NIR) region using supervised classifiers: Maximum Likelihood, Minimum Distance and SAM (Spectral Angle Mapper). The experiment is performed on CMU Hyperspectral Face Datasets (HFDS) within the spectral range of 610 to 1050 nm (VIR-NIR), having 45 spectral bands using ENVI 4.8 image processing tool. Supervised classification is performed using conventional supervised classifiers (MLH, MDA and SAM) on pre-processed HSI, further compressed dataset has classified by PCAMLH, PCAMD and PCASAM classifiers.

A probabilistic approach to classification of digital face images

A probabilistic approach to classification of digital face images

Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD

The paper posits a computationally-efficient algorithm for multi-class facial image classification in which images are constrained with translation, rotation, scale, color, illumination and affine distortion. The proposed method is divided into five main building blocks including Haar-Cascade for face detection, Bilateral Filter for image preprocessing to remove unwanted noise, Affine Speeded-Up Robust Features (ASURF) for keypoint detection and description, Vector of Locally Aggregated Descriptors (VLAD) for feature quantization and Cloud Forest for image classification. The proposed method aims at improving the accuracy and the time taken for face recognition systems. The usage of the Cloud Forest algorithm as a classifier on three benchmark datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising results. The proposed methodology using Cloud Forest algorithm successfully improves the recognition model by 2-12% when differentiated against other ensemble techniques like the Random Forest classifier depending upon the dataset used.

Face Classification Using Color Information

Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.

3D discrete wavelet transform‐based feature extraction for hyperspectral face recognition

Facial hyperspectral image analysis has become a popular topic since it provides additional spectral information on subjects unlike the 2D face imagery which only has spatial information, hence it has an opportunity to improve face recognition accuracy. Three new methods for feature extraction for facial hyperspectral image classification are proposed. The methods employ a three-dimensional discrete wavelet transform (3D-DWT) to extract features from facial hyperspectral images. One of the advantages of 3D-DWT for feature extraction in hyperspectral images is that the horizontal, vertical and spectral information are processed in parallel. The most important characteristic of 3D-DWT is decomposing hyperspectral images into a set of spatio-spectral frequency subbands. The study proposes three methods using 3D-DWT for feature extraction: 3D-subband energy, 3D-subband overlapping cube and 3D-global energy. The k-NN and collaborative representation-based classifier (CRC) are used to process extracted feature vector datasets, where classification accuracies are evaluated by four test scenarios. The results under different test scenarios revealed that accuracy of proposed 3D-DWT methods is superior to alternative methods using spatio-spectral classification.

Non-negative enhanced discriminant matrix factorization method with sparsity regularization

Efficient low-rank representation of data plays a significant role in the field of computer vision and pattern recognition. In order to obtain a more discriminant and sparse low-dimensional representation, a novel non-negative enhanced discriminant matrix factorization method with sparsity regularization is proposed in this paper. Firstly, the local invariance and discriminant information of the low-dimensional representation are incorporated into the objective function to construct a new within-class encouragement constraint term, and the weighted coefficients are introduced to further enhance the compactness between the samples that belong to the same class in the new base space. Secondly, a new between-class penalty term is constructed to maximize the difference between different classes of samples, and meanwhile, the weighted coefficients are introduced to further enhance the discreteness and discriminativeness between classes. Finally, to learn the part-based representation of data better, the sparse constraint term is further introduced, and consequently, the sparseness of data representation, the local invariance, and the discriminativeness are integrated into a unified framework. Moreover, the optimization solution and the convergence proof of objective function are given. The extensive experiments demonstrate the strong robustness of the proposed method to face recognition and image classification under occlusions.

Deep Metric Learning for Visual Understanding: An Overview of Recent Advances

In this article, we have summarized the recent trends of DML and shown their wide applications of various visual understanding tasks including face recognition, image classification, visual search, person reidentification, visual tracking, cross-modal matching, and image set classification. Empirical results have clearly demonstrated that DML can significantly improve the state of the art in these visual understanding tasks.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

Discriminative Histogram Intersection Metric Learning and Its Applications

In this paper, a novel method called discriminative histogram intersection metric learning (DHIML) is proposed for pair matching and classification. Specifically, we introduce a discrimination term for learning a metric from binary information such as same/not-same or similar/dissimilar, and then combine it with the classification error for the discrimination in classifier construction. Compared with conventional approaches, the proposed method has several advantages. 1) The histogram intersection strategy is adopted into metric learning to deal with the widely used histogram features effectively. 2) By introducing discriminative term and classification error term into metric learning, a more discriminative distance metric and a classifier can be learned together. 3) The objective function is robust to outliers and noises for both features and labels in the training. The performance of the proposed method is tested on four applications: face verification, face-track identification, face-track clustering, and image classification. Evaluations on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark, a dataset with more than 7 000 face-tracks, and Caltech-101 dataset validate the robustness and discriminability of the proposed metric learning, compared with the recent state-of-the-art approaches.

Using kernel sparse representation to perform coarse-to-fine recognition of face images

Feature space-based face recognition method performs representation and classification for face images in the feature space instead of the original space. This representation-based method uses kernel function to solve the feature space and therefore outperforms the method working in original space. A contemporary coarse-to-fine face recognition method also uses the idea of sparse representation but separates the classification into two stages from coarse candidate to fine classification. They both apply the sparse representation technique for image classification, but we believe that there is still space to improve the robustness of sparse representation in these methods. We proposed a novel method integrating both methods together to perform robust face image classification. This method also consists of two stages as sparse representation-based coarse-to-fine classification. In the first stage, however, the coarse candidate classes are determined in the feature space selected by the kernel function instead, which is the key technique used in the feature space-based method. And then in the next stage, fine classification is performed to assign the test sample into a class from candidate classes. The experimental results demonstrated that this novel integrated method outperforms both methods on prevailing benchmark facial datasets.

Gender Classification From The Iris Code Used For Recognition

In this paper, we present a new method for gender classification based on features of the iris texture selected by mutual information to improve gender classification of face images. The performance of the proposed approach has been investigated through extensive experiments performed on public database. We also showed improved results by fusion of texture features with best features selected independently from the left and right irises based on selection of features using rank feature selection method. The classification task has been achieved by using Support Vector Machine (SVM). We compared our method with existing gender classification methods based on texture feature with classifier being the same as SVM.

Ranking Tensor Subspaces in Weighted Multilinear Principal Component Analysis

Multilinear principal component analysis (MPCA) has been applied for tensor decomposition and dimensionality reduction in image databases modeled through higher order tensors. Despite the well-known attractive properties of MPCA, the traditional approach does not incorporate prior information in order to steer its subspace computation. In this paper, we propose a method to explicitly incorporate such semantics in the MPCA framework to allow an automatic selective treatment of the variables that compose the patterns of interest. The method relies on spatial weights calculated, in this work, by separating hyperplanes and Fisher criterion. In this way, we can perform feature extraction and dimensionality reduction taking advantage of high level information in the form of labeled data. Besides, the corresponding tensor components are ranked in order to identify the principal weighted tensor subspaces for classification tasks. In the computational results we consider gender and facial expression experiments to illustrate the capabilities of the method for dimensionality reduction, classification and reconstruction of face images.

An Improved Kernel Minimum Square Error Classification Algorithm Based on $L_{2,1}$ -Norm Regularization

The kernel minimum square error classification (KMSEC) algorithm has been widely used in classification problems. It shows a good performance on image data besides the following drawbacks: not sparse in the solutions and sensitive to noises. The latter drawback will result in a decrease in the recognition performance. To this end, we propose an improved (IKMSEC) by using the $L_{2,1}$ -norm regularization, which can obtain a sparse representation of nonlinear features to guarantee an efficient classification performance. The comprehensive experiments show the promising results in face recognition and image classification.