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In this paper, we present a method for face recognition using boosted Gabor feature based classifiers. Weak classifiers are constructed based on both magnitude and phase features derived from Gabor filters [6]. The multi-class problem is transformed into a two-class one of intra-and extraclass classification using intra-personal and extra-personal difference images, as in [12]. A cascade of strong classifiers are learned using bootstrapped negative examples, similar to the way in face detection framework [17]. The combination of classifiers based on two different types of features produces better results than using either type. Experiments on FERET database show good results comparable to the best one reported in literature [14].

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.

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