Machine Analysis of Facial Expressions

No abstract

Recognizing Surgically Altered Face Images and 3D Facial Expression Recognition

AbstractAltering Facial appearances using surgical procedures are common now days. But it raised challenges for face recognition algorithms. Plastic surgery introduces non linear variations. Because of these variations it is difficult to be modeled by the existing face recognition system. Here presents a multi objective evolutionary granular algorithm. It operates on several granules extracted from a face images at multiple level of granularity. This granular information is unified in an evolutionary manner using multi objective genetic approach. Then identify the facial expression from the face images. For that 3D facial shapes are considering here. A novel automatic feature selection method is proposed based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidian distances between 83 facial feature points in the 3D space. A regularized multi-class AdaBoost classification algorithm is used here to get the highest average recognition rate

Efficient smile detection by Extreme Learning Machine

Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration

Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin