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In this paper, a local appearance based face recognition algorithm is proposed. In the proposed algorithm local information is extracted using block-based discrete cosine transform. Obtained local features are combined both at the feature level and at the decision level. The performance of the proposed algorithm is tested on the Yale and CMU PIE face(More)
In this paper, the effects of feature selection and feature normalization to the performance of a local appearance based face recognition scheme are presented. From the local features that are extracted using block-based discrete cosine transform, three feature sets are derived. These local feature vectors are normalized in two different ways; by making(More)
Keywords: Face recognition from video Face detection Feature tracking Door monitoring Discrete cosine transform Fusion a b s t r a c t In this paper, we present a real-time video-based face recognition system. The developed system identifies subjects while they are entering a room. This application scenario poses many challenges. Continuous , uncontrolled(More)
Gaussian classifiers are strongly dependent on their underlying distance method, namely the Mahalanobis distance. Even though widely used, in the presence of noise this distance measure loses dramatically in performance, due to equal summation of the squared distances over all features. The features with large distance can mask all the other features so(More)
This paper presents an automatic video genre classification system, which utilizes several low level audio-visual features as well as cognitive and structural information, and in case of web videos tag-based features, to classify the types of TV programs and YouTube videos. Classification is performed using an ensemble of support vector machines. The visual(More)
This paper presents the results of our content–based video genre classification system on the 2012 MediaEval Tagging Task. Our system utilizes several low–level visual cues to achieve this task. The purpose of this evaluation is to assess our content–based system's performance on the large amount of blip.tv web–videos and high number of genres. The task and(More)
In this paper, we present the classification sub-system of a real-time video-based face identification system which recognizes people entering through the door of a laboratory. Since the subjects are not asked to cooperate with the system but are allowed to behave naturally, this application scenario poses many challenges. Continuous, uncontrolled(More)