Facial Feature Extraction Research Articles

Local Appearance based Novel Facial Feature Extraction Method for Human Expression Recognition

Local Appearance based Novel Facial Feature Extraction Method for Human Expression Recognition

Interest emotion recognition approach using self-organising map and motion estimation

Recognising human facial emotions by computer is an interesting problem. Although several approaches have been proposed the recognition rate, amount of used resources and calculation time remain factors for improvement. Our work presents a new approach for recognising basic emotions (joy, sadness, anger, disgust, surprise and fear) in image sequences. We introduced interest emotion and created its corresponding action units (AUs) based on psychological foundations. Our approach is mainly characterised by minimising used data and consequently optimising the computing time and improving the recognition rate. The proposed approach was divided into three steps: face detection using the Viola and Jones method, the extraction of facial features: here we exploited the facial action coding system, which is based on AUs. To detect AUs, we extracted face strategic points using an active appearance model and a block-matching approach. At the last, we classified the results by using the Kohonen self-organising map (SOM).

Classroom Micro-Expression Recognition Algorithms Based on Multi-Feature Fusion

The extraction of facial features is the key to micro-expression recognition. This paper puts forward a micro-expression recognition algorithm through multi-feature fusion. In this algorithm, the change of local binary pattern (LBP) feature distribution is correlated with projection error. For fast and accurate detection, the research data were all extracted from professional facial expression databases, the images had basically the same positions in each expression library, and the pure face image was acquired through manual segmentation from the selected expression library. Through comparison and its application in an intelligent classroom environment, it is proved that the proposed algorithm clearly outperforms the original LBP algorithm. The proposed method can be extended to other image recognition and classification problems.

Emotion Recognition Approach Using Multilayer Perceptron Network and Motion Estimation

Man-machine interaction is an interdisciplinary field of research that provides natural and multimodal ways of interaction between humans and computers. For this purpose, the computer must understand the emotional state of the person with whom it interacts. This article proposes a novel method for detecting and classify the basic emotions like sadness, joy, anger, fear, disgust, surprise, and interest that was introduced in previous works. As with all emotion recognition systems, the approach follows the basic steps, such as: facial detection and facial feature extraction. In these steps, the contribution is expressed by using strategic face points and interprets motions as action units extracted by the FACS system. The second contribution is at the level of the classification step, where two classifiers were used: Kohonen self-organizing maps (KSOM) and multilayer perceptron (MLP) in order to obtain the best results. The obtained results show that the recognition rate of basic emotions has improved, and the running time was minimized by reducing resource use.

Facial Recognition Based on DWT – HOG – PCA Features with MLP Classifier

Facial Recognition System has been widely used in various applications. Nevertheless, their efficiency rate fell dramatically when they were applied under unrestrained environments like the position of face, expression or illumination change. Because of these factors, it is essential to measure and calculate the performance rate of the dissimilar feature extraction techniques robust to such transformations in order to further integrate to a Facial Recognition System. This paper studies and evaluates the Histogram of Oriented Gradients method as a feature extraction method in order to deal with the abovementioned transformations. The study consists of four main phases: face detection, preprocessing, features extraction, and classification. Preprocessing is used to enhance the images by using the techniques of digital image processing. Feature extraction is used to get features from facial images based on the concept of Histogram Oriented Gradient feature that is applied to the facial image after conversion by means of the Discrete Wavelet Transform and vector reduction with the help of the Principle Component Analysis technique. Artificial neural network with the Back Propagation algorithm is used in training and testing as a classifier of the facial images to help recognize the face. To measure the performance of the method under consideration, some experiments were implemented using two datasets: ORL containing 400 facial images of 40 individuals that achieved the accuracy rate about 99.1%, and FERET containing 912 facial images of 152 individuals, which helped achieve accuracy rate about 94.5% at the multilayer perceptron neural network classifier.

Two-channel convolutional neural network for facial expression recognition using facial parts

This paper proposes the design of a facial expression recognition system based on the deep convolutional neural network by using facial parts. In this work, a solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method builds a two-channel convolutional neural network model in which facial parts are used as inputs: the extracted eyes are used as inputs to the first channel, while the mouth is the input into the second channel. Feature information from both channels converges in a fully connected layer which is used to learn global information from these local features and is then used for expression classification. Experiments are carried out on the Japanese female facial expression dataset and the extended Cohn-Kanada dataset to determine the expression recognition accuracy for the proposed facial expression recognition system based on facial parts. The results achieved shows that the system provides state of art classification accuracy with 97.6% and 95.7% respectively when compared to other methods.

A Privacy-Preserving Edge Computation-Based Face Verification System for User Authentication

The face recognition has become a common means of identity authentication because of the advantages of uniqueness, non-invasive and not easy to be stolen. The outsourcing of face recognition to the service provider is a typical manner nowadays. However, it raises vital concerns about the privacy of outsourcing server due to the sensitivity of face data. Therefore, a frame of identity authentication based on the technology of privacy-preserving face recognition is presented in this paper. The convolutional neural network is used for face feature extraction. To overcome the issue of privacy leaked, a method of secure nearest neighbor that can compute the cosine similarity over encrypted feature vectors is proposed. What's more, the edge computing is introduced in our frame to increase the authentication efficiency by removing some operations from the cloud to the edge of the Internet. Moreover, we also propose a secret sharing homomorphism technology which is used for distributed computing to improve the fault-tolerance of our identity authentication system. The experimental results show that the proposed schemes are secure and effective.

F2ConText: how to extract holistic contexts of persons of interest for enhancing exploratory analysis

A wide variety of publicly available heterogeneous data has provided us with an opportunity to meander through contextual snippets relevant to a particular event or persons of interest. One example of a heterogeneous source is online news articles where both images and text descriptions may co-exist in documents. Many of the images in a news article may contain faces of people. Names of many of the faces may not appear in the text. An expert on the topic may be able to identify people in images or at least recognize the context of the faces who are not widely known. However, it is difficult as well as expensive to employ topic experts of news topics to label every face of a massive news archive. In this paper, we describe an approach named F2ConText that helps analysts build contextual information, e.g., named entity context and geographical context of facial images found within news articles. Our approach extracts facial features of the faces detected in the images of publicly available news articles and learns probabilistic mappings between the features and the contents of the articles in an unsupervised manner. Afterward, it translates the mappings to geographical distributions and generates a contextual template for every face detected in the collection. This paper demonstrates three empirical studies—related to construction of context-based genealogy of events, tracking of a contextual phenomenon over time, and creation of contextual clusters of faces—to evaluate the effectiveness of the generated contexts.

Implementation of a TCM-based computational health informatics diagnostic tool for Sub-Saharan African students

Abstract Health status checkup is a crucial step towards early detection of diseases. Health status diagnosis, in university health centers, within the sub-Saharan African region, can be cumbersome and time consuming. In many cases, facilities for health checkup are not available. Traditional Chinese Medicine (TCM) is a promising approach, when integrated with in-silico methods. This study was conducted to implement a TCM-based computational health informatics diagnostic tool. The tool was applied to diagnose African students. This study was also conducted to stimulate further research into in-silico TCM diagnostics. Besides developing a reliable biometric verification system, to ascertain the real identities of patients brought to university health centers, it is assistive to create a platform that provides automated and complementary support for preliminary health diagnostic activities. It also mitigates stress, by helping to efficiently decipher and provide quick objective opinion from the perspective of a computerized decision support system. The diagnostic module of the computational health informatics diagnostic tool adopts knowledge from a TCM facial color diagnosis. A comprehensive literature search was conducted for relevant full-text research papers. Only research publications written in English language were reviewed. The present work was compared qualitatively and quantitatively with the existing works noted in the literature. Facial detection and matching algorithms were implemented for the TCM-based computational health informatics diagnostic tool by using Java programming language. Facial image acquisition processes were conducted. Captured facial images of African students were preprocessed. Facial feature extraction was performed by implementing feature extraction algorithms. An algorithm for the extraction of color information and measurement was also implemented. Knowledge of machine learning was applied to extract and collate facial features, and to machine learn from them. Facial classification and recognition algorithms were implemented. Finally, the results from the computational health informatics diagnostic tool were evaluated, by conducting a performance evaluation and validation. This study provides qualitative and quantitative information on facial recognition, facial color information measurement, as well as prediction of health status, for some sub-Saharan African University students. Performance evaluation was shown using confusion matrix and ROC curves. Statistical analysis of the experimental results was presented. The parameters in each diagnostic illustration were shown with valid range. In order to justify the effectiveness of the computational tool, further explanations were provided from relevant methodology guides on the evaluation of diagnostic tests. The computational health informatics diagnostic tool will complement the diagnostic efforts in university health centers of sub-Saharan African universities. It will also be useful for personal health diagnosis of interested individuals. The tool will also be viable for educating health professionals. TCM will be of immense benefit to developing countries by positively contributing towards diagnosing different non-communicable diseases and some infectious diseases in such countries.

Face Recognition Based on Fusion Feature of LBP and PCA with KNN

Face recognition mainly include face feature extraction and recognition (classification) two parts. The basic idea of local binary pattern (LBP) use texture description operator to build a number of local face description, then describe the local combination form global description. LBP operator works with the 3*3 neighborhood pixels for texture description. After scanning the whole image, we get a LBP response images. Then, we use principal component analysis (PCA) method to dimension reduction for response image, finally using K-Nearest Neighbor classifier to classify the test sample. The experimental results show that LBP-PCA-KNN method gains higher recognition rate than PCA-KNN method’s recognition rate, can be effectively used for face recognition.

Face feature extraction and recognition via local binary pattern and two-dimensional locality preserving projection

In this paper, we propose a novel face feature extraction approach based on Local Binary Pattern (LBP) and Two Dimensional Locality Preserving Projections (2DLPP) to enhance the texture features and preserve the space structure properties of a face image. LBP is firstly used to remove the effect of illumination and noise, which would enhance the detailed texture characteristics of face images. Then 2DLPP is performed to extract some prominent features and decrease the image dimension with space structure information. The Nearest Neighborhood Classifier (NNC) is used to recognize a face image at the end. In addition, the rule for dimension selection is studied from the results of experiments about choosing an appropriate feature dimension by 2DLPP computation. The experimental results on the Yale, the extended Yale B and CMU PIE C09 benchmark datasets showed that the proposed face feature extraction and recognition method achieves a better performance in comparison with similar techniques, and the proposed dimension selection rule can give an appropriate feature dimension in 2DLPP.

Face recognition using nonnegative matrix factorization with fractional power inner product kernel

Kernel nonnegative matrix factorization (KNMF) algorithms have been widely used to extract features for face recognition. The choice of kernel function is vital to facial feature extraction. The polynomial kernel function has been commonly used in KNMF. The power of the polynomial kernel is required to be a positive integer, thereby ensuring that the kernel generates a positive semi-definite matrix. In this paper, we investigate a new type of inner-product kernel that has a fractional power. The new kernel offers us flexibility in data representation as the power can be any positive real number. Based on the fractional power inner-product kernel, we present a novel KNMF algorithm called fractional power inner-product KNMF (FPKNMF). The FPKNMF algorithm is theoretically and experimentally validated to be convergent. The experimental results confirm that our algorithm exhibits a performance superior to the state-of-the-art methods in terms of facial representation and recognition accuracy.

A New Cost Function Combining Deep Neural Networks (DNNs) and l2,1-Norm with Extraction of Robust Facial and Superpixels Features in Age Estimation

Automatic age estimation from unconstrained facial images is a challenging task and it recently has gained much attention due to its wide range of applications. In this paper, we propose a new model based on convolutional neural networks (CNNs) and l2,1-norm to select age-related features for the age estimation task. A new cost function is proposed. To learn and train the new model, we provide the analysis and the proof for the convergence of the new cost function to solve minimization problem of deep neural networks (DNNs) and the l2,1-norm. High-level features are extracted from the facial images by using transfer learning, since there are currently not enough large age databases that can be used to train a deep learning network. Then, the extracted features are fed to the proposed model to select the most efficient age-related features. In addition, a new system that is based on DNN to jointly fine-tune two different DNNs with two different feature sets is developed. Experimental results show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database.

Research on face feature extraction based on K-mean algorithm

Face recognition technology is an application technology of information security, which is a kind of multi-disciplinary technology, such as comprehensive mathematics, pattern recognition, and biological characteristics. With the development of technology applications, the requirements for accuracy and anti-counterfeiting of face recognition are also increasing. In this paper, the K-mean algorithm is used to analyze the face features. Firstly, the biometric features of the face are extracted, and then the K-mean method is used to cluster the face features. Lastly, the SVM method is used to classify. The results show that the K-mean method can achieve high recognition performance with fewer feature numbers.

Extraction of Facial Features for Detection of Human Emotions under Noisy Condition

Extraction of Facial Features for Detection of Human Emotions under Noisy Condition

Location of Eyes in Images of Human Faces through Analysis Variance Shine Intensity

Extraction of facial features is an important step in automatic visual interpretation and recognition of human faces. Among the facial features, the eyes play a major role in the recognition process. In this article, we present an approach to detect and locate the eyes in frontal face images. Eye regions are identified using the technique of voucher detection based on mathematical morphology. After this identification is made a comparison between the variances of three different portions of each candidate region to eye (set of pixels belonging to the candidate region as a whole, set of pixels contained in a minimum rectangle circumscribed to the candidate region and set of pixels of the candidate region belonging to a horizontal band that crosses the center of mass of this region). The calculation of these variances also considers the R, G, and B channels, as well as the gray version of the input image.

Single Image 3D Face Reconstruction Based on Statistical Model

Single image 3D face reconstruction technology has a broad application in the life, also has a high technical difficulties. This paper proposes a single image 3D face reconstruction technology based on statistical model. The method established 3D face statistical model based on 3D face database in advance, and trained 2D facial feature points parameter model based on the 2D face database using the algorithm of SDM. when reconstruct 3D face model from single image, first we use 2D facial feature points parameter model to extract face feature points; Then, according to 3D face model, we use adaptive learning factor gradient descent method to iteratively optimize the energy function, to get statistical model parametric vector, namely got the 3D face model corresponding to the 2D face images. The results show that use the proposed method to rebuild the faces have high similarity.

Normalization of face illumination using basic knowledge and information extracted from a single image

This paper presents a method for face image normalization that can be applied to the extraction of illumination invariant facial features or used to remove bad lighting effects and produce high-quality, photorealistic results. Most of the existing approaches concentrate on separating the constant albedo from the variable light intensity; that concept, however, is based on the Lambertian model, which fails in the presence of specularities and cast shadows. Therefore, to tackle this problem, various methods use bootstrap sets to learn the reflectance model of a given person or a face in general. Unfortunately, algorithms of this type are usually not practical for real applications. The proposed approach does not require any training procedure, as the normalization is performed only on the basis of information that is contained in the image that is being processed. Still, external knowledge that is represented by the deformable shape model is employed to execute the localization of the important points, such as eye centers. Additionally, an assumption regarding the symmetry of the face is utilized. The rest of the normalization algorithm relies mostly on simple image processing techniques. Experiments on the CMU-PIE and Extended Yale B databases show that the new method not only provides satisfactory recognition results but is also able to generate natural-looking images.

Face recognition system based on MOTIF features

ABSTRACTThis 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.

Facial age feature extraction based on deep sparse representation

In recent years, the research on facial age estimation has attracted more and more interests from the scholars and researchers in the field of psychological, aesthetic, forensic science and computer vision. However, facial age estimation is a challenging work: face age does not suffer from only the influence of intrinsic factors (e.g., genes) but also external factors (e.g., living conditions), so that it is difficult to find accurate features which can describe the change of age. Therefore, this paper proposes a robust face feature extraction method based on dynamic deep sparse representation. Combined with the respective characteristics of Active Appearance Model (AAM), Local Binary Patterns (LBP), Gabor and Bio-Inspired Features (BIF), this method will give sufficient consideration to the thinking way of object recognition of the human, similarity of adjacent ages and the classification principle of signal sparse representation. In addition, in order to reduce the interference of the face identity factor, two factors analysis method is proposed to separate the face identity factor. The experimental results show that the feature extraction method proposed in this paper has strong discrimination and robustness, which outperforms the state-of-the-art age estimation approaches.