Self-organizing adaptation for facial emotion mapping

This paper presents an emotion mapping system that attempts to emulate human brain reference model. The system first locates the human face in an image, and then identifies the localized face emotion. The understanding of cognitive system is presented in the paper. It highlights how individual module is mapped to the proposed system. Then, single- and multi-layer self-organizing emotion maps are described. The system is evaluated through various test sets. The experimental results show encouraging hit rates for identifying emotions of unknown subjects

Convolutional Neural Network for Face Recognition with Pose and Illumination Variation

Face recognition remains a challenging problem till today. The main challenge is how to improve the recognition performance when affected by the variability of non-linear effects that include illumination variances, poses, facial expressions, occlusions, etc. In this paper, a robust 4-layer Convolutional Neural Network (CNN) architecture is proposed for the face recognition problem, with a solution that is capable of handling facial images that contain occlusions, poses, facial expressions and varying illumination. Experimental results show that the proposed CNN solution outperforms existing works, achieving 99.5% recognition accuracy on AR database. The test on the 35-subjects of FERET database achieves an accuracy of 85.13%, which is in the similar range of performance as the best result of previous works. More significantly, our proposed system completes the facial recognition process in less than 0.01 seconds

Forensic Face Sketch Recognition Using Computer Vision

Now - a - days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each - other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However studies say that hand - drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victims sketches on behalf of the descripti on provided by an eyewitness. Sometimes suspects used special mask to hide some common features of faces like nose, eyes, lips, face - color etc. but the outliner features of face biometrics one could never hide. In this work, I concentrated on some specific facial geometric feature which could be used to calculate some ratios of similarities from the template photograph database against the forensic sketches. This paper describes the design of a system for forensic face sketch recognition by a computer visi on approach like Two - Dimensional Discrete Cosine Transform (2D - DCT) and the Self - Organizing Map (SOM) Neural Network simulated in MATLAB

Deteksi Kantuk pada Pengemudi Berdasarkan Penginderaan Wajah Menggunakan PCA dan SVM

Drowsiness while driving is one of the main causes of traffic accidents it affects the level of focus of the driver. Therefore, we need an automatic drowsiness detection mechanism for the driver to provide a warning or alarm so that an accident can be avoided. In this study, we design and simulate a system to detect drowsiness through the driver’s yawn expression. The acquisition is made by recording the face from two shooting points including the dashboard and front mirrors in the car. From the video recording, then it is taken into several images with a size of 128x82 pixels which are used as training and testing data. This image is then processed using Principal Component Analysis (PCA) for feature extraction and classified using a Support Vector Machine (SVM). From the tests carried out, the system generates the highest accuracy of 98%. This best performance is obtained by SVM with polynomial kernel in the camera position on the dashboard. Meanwhile, based on compression testing, the image that can still meet system requirements is 25% of the original size. It is hoped that the proposed drowsiness detection method in this study can be applied for real-time drowsiness detection in vehicles.

An Intelligent Facial Recognition System using Stacked Auto Encoder with Convolutional Neural Network (CNN) Approach

The act of identifying an emotional feeling  is described as facial expression.  one of the effective techniques for interperson communication. They serve as indications that regulate interactions with those around. As a result, they are crucial in creating effective relationships.Facial expression recognition system to identify the expressions by evaluating the changes in facial characteristics and extracting features from facial images. This system  essential for enhancing computer-human interaction. The majority of facial emotion recognition research mainly relies on  reference face model and well known facial landmarks. Due to  intricacy of the face musculature, finding the most noticeable facial landmarks can be difficult and requires physical intervention for improved accuracy. So, this research work provides  new dimension to deal with the above issues by proposing a Stacked Auto-Encoder with Convolutional Neural Network based approach that does not rely on the landmarks or a reference model. The proposed approach outperforms the existing techniques