Prominent Boundaries and Foreground Detection Based Technique for Human Face Extraction from Color Images Containing Complex Background

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications including, but not limited to, human behaviour understanding, detection of mental disorders, and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in four-stages consisting of pre-processing, face detection, feature extraction, and expression classification. The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control

Facial expressions recognition and synthesis are important research fields to study how human beings reflect to environments in affective computing. With the rapid development of mathematical theory on multivariate statistics and multi-media technology especially image processing, facial expressions recognition researchers have achieved many useful results. Recently studies show that approaches to facial modeling and expressions recognition and synthesis analysis could be adapted to control security or even real-time health monitoring in the real world. Similarity, how to achieve facial expressions recognition and synthesis for independent-free mechanism is a central design in general. Based on the cognitive analysis of independent user's affective facial recognition researches we proposed an emotions composition model to dope out what are the user's new affective facial expressions. At this point, principal component, cluster and discriminate analysis were applied to show and verify independent user's affective expressions can be composited for synthesis by basic facial expressions such as happy, neutral and unhappy. Experiments were conducted to prove our models to be very significant.

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Two completely different methods are proposed for face detection in this paper. Our proposed methods do not use the skin colour information for face detection. The detection of faces is accomplished after cancelling the effect of the various illumination conditions. Because of the independency of the approaches to the face's skin colour, persons with every kind of skin colours are detected even in completely dark environments. Next, the detected faces are recognized. The experiments have shown that we can combine some proposed aspects of feature based methods with eigenface method and get successful results. The illumination dependency and scaling problems of eigenface method have also been solved by a new methodology in this paper. (9 pages)

Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature space is developed. Least squares estimation approach that utilizes interdependency between points in training patterns is used to form the nonlinear region. The proposed feature extraction strategy is employed to improve face recognition accuracy under varying illumination conditions and facial expressions. Though the face features show variations under these conditions, the features of one individual tend to cluster together and can be considered as a neighborhood. Low dimensional representations of face patterns in the feature space may lie in a nonlinear constraint region, which when modeled leads to efficient pattern classification. A feature space encompassing multiple pattern classes can be trained by modeling a separate constraint region for each pattern class and obtaining a mean constraint region by averaging all the individual regions. Unlike most other nonlinear techniques, the proposed method provides an easy intuitive way to place new points onto a nonlinear region in the feature space. The proposed feature extraction and classification method results in improved accuracy when compared to the classical linear representations. Face recognition accuracy is further improved by introducing the concepts of modularity, discriminant analysis and phase congruency into the proposed method. In the modular approach, feature components are extracted from different sub-modules of the images and concatenated to make a single vector to represent a face region. By doing this we are able to extract features that are more representative of the local features of the face. When projected onto an arbitrary line, samples from well formed clusters could produce a confused mixture of samples from all the classes leading to poor recognition. Discriminant analysis aims to find an optimal line orientation for which the data classes are well separated. Experiments performed on various databases to evaluate the performance of the proposed face recognition technique have shown improvement in recognition accuracy, especially under varying illumination conditions and facial expressions. This shows that the integration of multiple subspaces, each representing a part of a higher order nonlinear function, could represent a pattern with variability. Research work is progressing to investigate the effectiveness of subspace projection methodology for building manifolds with other nonlinear functions and to identify the optimum nonlinear function from an object classification perspective.

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

This paper pertains to the recognition of human face within the color images. The proposed scheme used the clue of skin color to detect faces within the color images, and Mallat wavelet transform was applied to get the LL2 frequency subband image of the original faces, then, features were extracted from the raw data by using principal component analysis (PCA). These discriminative and low dimensional features achieved were fed to a support vector machines (SVMs) with polynomial kernel for the training and fuzzy theory was applied on multi-class classification to deal with the unclassifiable cases. Experiments were conducted using the indoors photographs and the results demonstrated that the proposed methodology was efficient for the frontal faces detection and recognition.

Human face detection is the most important process in applications such as video surveillance, human computer interface, face recognition, and image database management. Face detection algorithms have primary factors that decrease a detection ratio : variation by lighting effect, location and rotation, distance of object, complex background. Due to variations in illumination, background, visual angle and facial expressions, the problem of machine face detection is complex. Algorithms were discussed in several papers about face detection and face recognition. But we know that implementation of these algorithm is not easy. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex background. We use the YCbCr color space since it is widely used in video compression standard and multimedia streaming services. Our method detects skin regions over the entire image, and then generates face candidate based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, nose, and boundary maps for verifying each face candidate.

Recently, the application of hand recognition and face recognition have been installed and run in smart devices to response some demands of human life. However, in the process of recognition, there still exist difficulties such as illumination, complex background and speed in real time mode. In this paper, we propose a face and hand gesture recognition for secure control of equipment. Facial and hand gesture recognitions are combined to secure and control electrical devices. Combination between facial and hand gesture recognition still have some challenges such as same shape and same skin color between hand and face, etc. Main goal of this study is to improve the recognition ability with excellent accuracy rate, and speed up the process rate in order to satisfy real time mode in some application systems.

A novel approach for human face detection from color images under complex background

A human face has a distinct and unique characteristics which make it play a very critical role in recognizing facial expression in a “facial expression recognition system.” Identifying or as we say it detection of expressions plays a big and significant role in a facial expression recognition system. If we talk about a human being it becomes an easy task recognize expression in any particular image sequence, but at the same time if we talk about fully automated systems not many are currently available or capable to do so. The field of facial expression recognition do have many different applications and its importance, it might be used to have an interaction between a human being and a computer, here a user, without using his hands, can give commands or instruct to the computer system with the help of facial expression recognition system. Quite a few options are available to identify a face in an image in an efficient and accurate manner, although similar cannot be said for features detection in a video sequence frame. Most systems are still dependent on manual operations for same. Here in this paper we have emphasizes on color normalization and facial feature extraction which uses LBP (Local Binary Pattern) as an effective feature detection approach, where the existing algorithms have been modified to improve the facial expression recognition accuracy. The recognition accuracy on the Indian database is observe to be 94.7\%.

Cognitive domains related to attention and executive functions (a set of cognitive processes that regulate, control, and manage other cognitive abilities) seem to influence the recognition of facial expressions in people with Alzheimer's disease (AD). We examined the relationship between facial expression recognition, global cognition and executive function in people with AD according to their cognitive level. In a cross-sectional design, we included 130 participants with AD divided into three groups based on their Mini-Mental State Examination (MMSE) scores: MMSE 1 (scores 23-28), MMSE 2 (scores 17-22), and MMSE 3 (scores 11-16). Facial expression recognition ability was analyzed using the Faces Test. Executive function was analyzed using the Trail Making Test (TMT), the Verbal Fluency Test (VFT), the Semantic Fluency Test (SFT), the Digit Span Forward (DSF) and Backward (DSB) tests, and the Clock Drawing Test (CDT). In MMSE 1 group difficulties in divided attention and cognitive flexibility impacted the accuracy of facial expression recognition. In the MMSE 2 group, facial expression recognition was related to impairment in working memory. In the MMSE 3 group, the impact on facial expression recognition was directly related to visuoconstructive abilities. We observed that the executive resources involved in each evaluated group differed in terms of facial recognition task performance efficacy. Interventions at stimulating executive and visuoconstructive abilities in people with AD may contribute to better preservation of facial expression recognition.

Face recognition has many important applications in areas such as public surveillance and security, identity verification in the digital world, and modeling techniques in multimedia data management. Facial expression recognition is also important for targeted marketing, medical analysis, and human–robot interaction. In this paper, we survey a few techniques for facial analysis. We compare the cloud platform AWS Rekognition, convolutional neural networks, transfer learning from pre-trained neural nets, and traditional feature extraction using facial landmarks for this analysis. Although not comprehensive, this survey covers a lot of ground in the state-of-the-art solutions for facial analysis. We show that to get high accuracy, good-quality data and processing power must be provided in large quantities. We present the results of our experiments which have been conducted over six different public as well as proprietary image data sets.

Facial Expression Recognition (FER), also known as Facial Emotion Recognition, is an active topic in computer vision and machine learning fields. This paper analyzes different feature extraction and classification methods to propose an efficient facial expression recognition system. We have studied several feature extraction methods, including Histogram of Oriented Gradients (HOG), face-encoding, and the features extracted by a VGG16 Network. For classification, different classical classifiers, including Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), and Logistic Regression, are evaluated with these features. Besides, we have trained a ResNet50 model from scratch and also tuned a ResNet50 which is pre-trained on VGGFace2 dataset. Finally, a part-based ensemble classifier is also proposed by focusing on different parts of face images. The experimental results provided on FER-2013 Dataset show that the tuned model of ResNet50 with a complete image of face, achieves higher performance than the other methods.

We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.