Facial expressions

In recent years, much focus has been put on employing technology to make novel behavioral aids for those with autism. Most of these are digital adaptations of tools used in standard behavioral therapy to enforce normative skills. These digital counterparts are often used outside of both the larger therapeutic context and the real world, in which the learned skills might apply. To address this, we are designing a system of automatic expression recognition on wearable devices that integrates directly into the families' daily social interactions, to give children and their caregivers the tools and information they need to design their own holistic therapy. In order to develop a tool that will be truly useful to families, we proactively include children with autism and their families as co-designers in the development process. By providing an app and interface with interchangeable social feedback options, we aim to produce a framework for therapy that folds into their daily lives, tailored to their specific needs.

Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features-which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images. I. INTRODUCTION In the present day scenario, lot of research is going in the field of face recognition. The aim of face recognition is to match the input image and the image stored in database [1]. Face recognition has played a vital role in solving the problem of criminal identification, human computer interaction. The procedure of dimensionality reduction is a crucial phase in face recognition task where the data have an inherent high dimensionality. For diminishing the dimensionality of image space, PCA is applied [2]. The representation of face by means of Gabor features has caused huge interest in computer vision, image processing, and pattern recognition and further. The major inspiration for using the Gabor filter is its biological significance. The accessible field profile of neurons in the principal visual cortex of mammals is oriented having the features of spatial frequencies. Gabor filter can also make use of prominent visual properties such as spatial frequency feature, orientation selectivity and spatial localization [3]. Face conveys emotions more rapidly than people realize their feelings. Facial expression reveals one's emotions and provides essential communicative indications throughout social communication. This shows that facial expression figures a foremost modality. This research turns out to be progressively more admired in computer vision or in area of robotics. Face recognition and identification have applications like image retrieval, surveillance and automated access control. The analysis of facial expressions has extensive variety of applications such as image understanding, psychological area, face animation, human computer interaction (HCI), etc. Interaction takes place both verbally and non-verbally in humans. The automatic facial expression recognition comprises of three main components for attaining this objective. Firstly, the face is identified and established. Facial features are extracted from the identified face region. Lastly, the facial expression category is classified on the basis of extracted features. For identification of face regions from image, various practices are implemented now-a-days [4]. A human face has important and exclusive features due to which the detection of expressions of human faces is possible. The transformation that takes place in answer to human internal emotional status is referred as facial expression recognition (FER). This make use of diverse applications of Human Computer Interaction (HCI) like facial video surveillance system, facial animation and face image processing in the area of Artificial Intelligence, computer vision and digital image processing. For the past few years Automatic Recognition has become an area of great concern and is a difficult task too. The feature extraction phase plays an important role in FER. The facial expression, puts 55% to the total communication, while verbal and vocal contributes 7% and 38% in given order [5]. Facial expression conveys rich information regarding human emotions and plays vital role in human communication. They regulate to smoothen the process for more intellectual communication [6]. Regular facial expression recognition usually comprises of three fundamental steps: face acquisition, facial feature extraction and facial expression classification. Face acquisition is a preprocessing phase to perceive or establish face from the entered series or images. The most extensively preferred face detector is the real-time face detection algorithm. Different classifiers are used which have Harr-wavelet characteristics. Once the face is identified in database images, the equivalent face regions are frequently standardized to have the same gray level and same eye distance. The facial characteristics withdrawal efforts are done to discover the most suitable illustration of facial images for detection. There are mainly two approaches: appearance features-based systems and geometric features-based systems.