Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics

微笑作为一种典型的生物多样性特征信号,在社会交往中有较大影响力,它揭示了人的情感感受和内心状态。自发性的微笑与假笑由不同大脑系统发出,在形态学和动力学上均存在差异。区分这两种类型的微笑仍具有挑战性,因为其中细微差别很难被肉眼观察到,仍有待被识别捕捉。已有相关研究大多是提取自发性微笑的几何特征,而外观特征并没有被充分利用,导致纹理信息的丢失。本文提出一种基于特定区域纹理描述来表示不同面部区域的局部模式变化,从而弥补几何特征研究的局限性。每个面部区域的时间相位是通过计算相应的面部区域强度来划分,而非仅考虑嘴巴区域强度。同时利用支持向量机的中层融合策略,将两种特征类型结合起来。实验结果表明,本文提出的外观表示法及其与基于几何形状的人脸动力学的结合技术,在BBC、SPOS、MMI和UvA‐NEMO四个基准数据库中得到很好的应用。

An immune local concentration based virus detection approach

Along with the evolution of computer viruses, the number of file samples that need to be analyzed has constantly increased. An automatic and robust tool is needed to classify the file samples quickly and efficiently. Inspired by the human immune system, we developed a local concentration based virus detection method, which connects a certain number of two-element local concentration vectors as a feature vector. In contrast to the existing data mining techniques, the new method does not remember exact file content for virus detection, but uses a non-signature paradigm, such that it can detect some previously unknown viruses and overcome the techniques like obfuscation to bypass signatures. This model first extracts the viral tendency of each fragment and identifies a set of statical structural detectors, and then uses an information-theoretic preprocessing to remove redundancy in the detectors’ set to generate ‘self’ and ‘nonself’ detector libraries. Finally, ‘self’ and ‘nonself’ local concentrations are constructed by using the libraries, to form a vector with an array of two elements of local concentrations for detecting viruses efficiently. Several standard data mining classifiers, including K -nearest neighbor (KNN), radial basis function (RBF) neural networks, and support vector machine (SVM), are leveraged to classify the local concentration vector as the feature of a benign or malicious program and to verify the effectiveness and robustness of this approach. Experimental results show that the proposed approach not only has a much faster speed, but also gives around 98% of accuracy.

Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.