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Facial expression recognition (FER) is a crucial technology and a challenging task for human-computer interaction. Previous methods have been using different feature descriptors for FER and there is a lack of comparison study. In this paper, we aim to identify the best features descriptor for FER by empirically evaluating five feature descriptors, namely Gabor, Haar, Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and Binary Robust Independent Elementary Features (BRIEF) descriptors. We examine each feature descriptor by considering six classification methods, such as k-Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Adaptive Boosting (AdaBoost) with four unique facial expression datasets. In addition to test accuracies, we present confusion matrices of FER. We also analyze the effect of combined features and image resolutions on FER performance. Our study indicates that HOG descriptor works the best for FER when image resolution of a detected face is higher than 48×48 pixels.

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Most literatures have been relying on image processing approaches such as skin detection and depth thresholding for hand detection. These techniques are restricted by strong assumptions and normally possess low robustness in actual applications. In this paper, we focus on an appearance approach and propose a new feature extraction method based on sparse pixel-pairwise intensity comparisons for hand detection. Our method can be viewed as a generalized BRIEF descriptor and can be easily adopted for other object detection or recognition tasks. We perform extensive experiments and prove that our method achieves comparable results with normal, noisy, and occluded hand images in term of both test accuracy and ROC. The main contributions of our work are threefold: 1) We introduce a new and simple feature extraction method that is robust against image noise, cluttered backgrounds, and partial occlusion. 2) Combined with AdaBoost, we show that the new feature descriptor is effective for hand detection. 3) The new feature descriptor has been rigorously compared with existing feature descriptors with a new hand database that has very challenging image backgrounds.

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SUMMARYRandom forest regressor has recently been proposed as a local landmark estimator in the face alignment problem. It has been shown that random forest regressor can achieve accurate, fast, and robust performance when coupled with a global face-shape regularizer. In this paper, we extend this approach and propose a new Local Forest Classification and Regression (LFCR) framework in order to handle face images with large yaw angles. Specifically, the LFCR has an additional classification step prior to the regression step. Our experiment results show that this additional classification step is useful in rejecting outliers prior to the regression step, thus improving the face alignment results. We also analyze each system component through detailed experiments. In addition to the selection of feature descriptors and several important tuning parameters of the random forest regressor, we examine different initialization and shape regularization processes. We compare our best outcomes to the state-of-the-art system and show that our method outperforms other parametric shape-fitting approaches. key words: face alignment, shape model fitting, random forest, point distribution model, sparse representation model

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