Facial identification has been recognized as most simple and non-intrusive technology that can be applied in many places. However, there are still many unsolved facial identification problems due to different intra-personal variations. In particular, when images of the databases appear at different facial expressions, most currently available facial recognition approaches encounter the expression-invariant problem in which neutral faces are difficult to be recognized. In this paper, a new approach is proposed to transform facial expressions to neutral-face like images; hence enabling image retrieval systems to robustly identify a person's face for which its learning and testing face images differ in facial expression.
Email is a commonly used tool for communication which allows rapid and asynchronous communication.The growing popularity and low cost of e-mails have made spamming an extremely serious problem today. Several anti-spam filtering techniques have been developed but most of them suffer from low accuracy and high false alarm rate due to complexity and changing nature of unsolicited messages. This study proposes an innovative classification framework with comparable accuracy, affordable computation and high system robustness. In particular, an effective feature selection scheme is implemented in conjunction with an adjustable combination of linear and nonlinear learning algorithms. Extensive experiments have indicated that the proposed framework compares favorably to other state-of-the-art methods, especially when misclassification cost is high.
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