Face detection in profile views using fast discrete curvelet transform (FDCT) and support vector machine (SVM)

Human face detection is an indispensable component in face processing applications, including automatic face recognition, security surveillance, facial expression recognition, and the like. This paper presents a profile face detection algorithm based on curvelet features, as curvelet transform offers good directional representation and can capture edge information in human face from different angles. First, a simple skin color segmentation scheme based on HSV (Hue - Saturation - Value) and YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in color images with good detection rate and low misdetection rate

The Possibilities of Classification of Emotional States Based on User Behavioral Characteristics

The classification of user's emotions based on their behavioral characteristic, namely their keyboard typing and mouse usage pattern is an effective and non-invasive way of gathering user's data without imposing any limitations on their ability to perform tasks. To gather data for the classifier we used an application, the Emotnizer, which we had developed for this purpose. The output of the classification is categorized into 4 emotional categories from Russel's complex circular model - happiness, anger, sadness and the state of relaxation. The sample of the reference database consisted of 50 students. Multiple regression analyses gave us a model, that allowed us to predict the valence and arousal of the subject based on the input from the keyboard and mouse. Upon re-testing with another test group of 50 students and processing the data we found out our Emotnizer program can classify emotional states with an average success rate of 82.31%

Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time