Facial Feature Selection Research Articles

Face image profiles features extraction for recognition systems

The object of research is the algorithm of piecewise linear approximation when applying it to the selection of facial features and compression of its images. One of the problem areas is to obtain the optimal ratio of the degree of compression and accuracy of image reproduction, as well as the accuracy of the obtained facial features, which can be used to search for people in databases. The main characteristics of the image of the face are the coordinates and size of the eyes, mouth, nose and other objects of attention. Dimensions, distances between them, as well as their relationship also form a set of characteristics. A piecewise linear approximation algorithm is used to identify and determine these features. First, it is used to approximate the image of the face to obtain a graph of the silhouette from right to left and, secondly, to approximate fragments of the face to obtain silhouettes of the face from top to bottom. The purpose of the next stage is to implement multilevel segmentation of the approximated images to cover them with rectangles of different intensity. Due to their shape they are called barcodes. These three stages of the algorithm the faces are represented by two barcode images are vertical and horizontal. This material is used to calculate facial features. The medium intensity function in a row or column is used to form an approximation object and as a tool to measure the values of facial image characteristics. Additionally, the widths of barcodes and the distances between them are calculated. Experimental results with faces from known databases are presented. A piecewise linear approximation is used to compress facial images. Experiments have shown how the accuracy of the approximation changes with the degree of compression of the image. The method has a linear complexity of the algorithm from the number of pixels in the image, which allows its testing for large data. Finding the coordinates of a synchronized object, such as the eyes, allows calculating all the distances between the objects of attention on the face in relative form. The developed software has control parameters for conducting research.

Feature selection for face authentication systems: feature space reductionism and QPSO

In face authentication systems, the feature selection (FS) process is very important because any feature extractor introduces some irrelevant or noisy features. These features can affect in the performance of such systems. In this paper, a new method is proposed to reduce the computations time in the facial feature selection. Quantum Fourier transforms (QFT), discrete wavelet transform (DWT). Discrete cosine transform (DCT) and scale invariant feature transform (SIFT) are employed separately as features' extractors. The proposed algorithm denoted by FSR_QPSO has two phases: feature space reductionism (FSR) and optimal feature selection based on quantum particle swarm optimisation (QPSO). FSR reduces the size of the feature matrix by selecting the best vectors (rows) and rejects the worst. Then QPSO is applied to fetch the optimal features set over the reduced space that contains the best vectors only. The proposed algorithm has been tested on ORL and Face94 databases. The experimental results show that the proposed algorithm reduces feature selection time against the case of using complete feature space.

Feature Selection and Recognition of Face by using Hybrid Chaotic PSO-BFO and Appearance-Based Recognition Algorithms

Swarm intelligence based approaches are a recent optimization algorithm that simulates the groups (collective) behavior of decentralized and self-organized systems and have gained more proliferation due to a variety of applications and uses in the feature selection to solve the complex problems and classify the objects based on chosen optimal set of features. Feature selection is a process that selects a subset from the extracted features sets according to some criterions for optimization. In computer vision based face recognition systems, feature selection, and representation algorithms play an important role for the selection of optimal, and discriminatory sets of facial feature vectors from the face database. This paper presents a novel approach for facial feature selection by using Hybrid Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) optimization algorithms. The hybrid approach consists of two parts: (1) two types of chaotic mappings are introduced in different phase of proposed hybrid algorithms which preserve the huge diversity of population and improve the global searching and exploration capability; (2) In proposed hybrid approach, appearance based (holistic) face representation and recognition approaches such as Principal Component Analysis (PCA), Local Discriminant Analysis (LDA), Independent Component Analysis (ICA) and Discrete Cosine Transform (DCT) extract feature vectors from the Yale face database. Then features are selected by applying hybrid Chaotic PSO and BFO algorithms for the selection of optimal set of features; it quickly searches the feature subspace of facial features that is the most beneficial for classification and recognition of individuals. From the experimental results, the authors have compared the performance of proposed hybrid approach with existing approaches and conclude that hybrid approach can be efficiently used for feature selection for classification and recognition of face of individuals.

Face Detection and Pose Estimation Based on Evaluating Facial Feature Selection

The detection of faces is one of the most requesting fields of research in image processing and Visual estimation of head pose is desirable for computer vision applications such as face recognition, human computer interaction, and affective computing. In this paper, we propose completed method for face pose estimation, face and face parts detection, feature extraction, tracking. This paper proposes using an improved AdaBoost algorithm, which is much better than normal AdaBoost. We use the de-facto Viola-Jones method for face and face part detection. From the robustness property of Haar-like feature, we first construct the strong classifier more effective to detect rotated face, and then we propose a novel method that can reduce the training time. We adopt affine motion model estimation as a tracking method. The combination enables efficient detection around the search area limited by tracking. Experimental results demonstrated its effectiveness and robustness against different types of detection and pose estimation in the input face images, including faces that appear in a wide range of image positions and scales, and also complex backgrounds, occlusions, illumination variations and multi-pose head images.

A Method for Human Emotion Recognition System

This paper studied computationally efficient algorithm for facial feature selection based on template matching method which further leads to identification of sm iling face or neutral face. At first, minimal pre-processing including gray scale conversion is done on the image. After that, matching between original image and template image is done using normalized cross-correlation technique. Each matching area is bounded by box to identify that region of interest. The n the mid points between the eye regions are found and the distance between the mid points and the corne rs of the mouth region is calculated. On the basis of the distances between these features, emotions are recognized. After detecting neutral or smiling face, different types of facial expressions are classi fied using linear support vector machine used as multiclass classifier.

Facial Expression Recognition Based on Sparse Locality Preserving Projection

In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.

Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

This paper incorporates principal component analysis (PCA) with support vector machine-particle swarm optimization (SVM-PSO) for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

Facial identity and facial expression matching in 5–12‐year‐old children and adults

AbstractFacial identity and facial expression matching tasks were completed by 5–12‐year‐old children and adults using stimuli extracted from the same set of normalized faces. Configural and feature processing were examined using speed and accuracy of responding and facial feature selection, respectively. Facial identity matching was slower than face expression matching for all age groups. Large age effects were found on both speed and accuracy of responding and feature use in both identity and expression matching tasks. Eye region preference was found on the facial identity task and mouth region preference on the facial expression task. Use of mouth region information for facial expression matching increased with age, whereas use of eye region information for facial identity matching peaked early. The feature use information suggests that the specific use of primary facial features to arrive at identity and emotion matching judgments matures across middle childhood. Copyright © 2009 John Wiley & Sons, Ltd.

Selection and fusion of facial features for face recognition

This paper proposes and investigates a facial feature selection and fusion technique for improving the classification accuracy of face recognition systems. The proposed technique is novel in terms of feature selection and fusion processes. It incorporates neural networks and genetic algorithms for the selection and classification of facial features. The proposed technique is evaluated by using the separate facial region features and the combined features. The combined features outperform the separate facial region features in the experimental investigation. A comprehensive comparison with other existing face recognition techniques on FERET benchmark database is included in this paper. The proposed technique has produced 94% classification accuracy, which is a significant improvement and best classification accuracy among the published results in the literature.

An economised craniofacial identification system

It has been attempted to develop an economised craniofacial identification system, as a special automated version of photo/video superimposition technique, that can deal with common cases of personal identification with the aid of a skull and a nearly front view face photograph of the suspected victim. The proposed method is economic in respect of (i) cost of hardware configuration, (ii) processing time as well as (iii) manual labour involved. Over and above, it has got a capability to take care of ambiguities due to soft tissue thickness during the selection of facial features, which is a part of the procedure. In order to reconstruct a 2-D cranial image, superimposable over the facial one, the new method does not need any reconstruction of a digitised 3-D cranial image. It works simply by a suitable segment-wise processing of a 2-D cranial image with the aid of the symmetry perceiving adaptive neuronet (SPAN), that has recently been introduced in connection with nearly front view facial image recognition. The final comparison of the facial and the superimposable cranial images is as versatile as the same for facial image recognition by SPAN. A practical application of this extended version of SPAN has been demonstrated in the present paper.