Search

In face verification applications, precision rate and identifying liveness are two key factors. Traditional methods usually recognize global faces and can not gain good enough results when the faces are captured from different ages, or there are some interference factors, such as facial shade, etc. Besides, the false face attack will pose a great security risk. To solve the above problems, this study examines how to achieve reliable living face verification based on Multi-CNNs (convolutional neural networks) and Bayes probability. Participants are required to make several expressions in random orders and contents, to ensure their liveness. Then, an effective component-based method is proposed for face recognition, and synthesized multiple CNNs can help reflecting intensities of different components. Eventually, the similarity between faces is calculated by integrating the results of each CNN, with the help of Bayes probability. Comparative experiments demonstrate that our algorithm outperforms traditional methods in face recognition accuracy. Moreover, our algorithm has unique preponderance in that it can verify the liveness of users, which can achieve higher security.

Finger-vein verification is an emerging biometrics technology. Its first task is extracting finger-vein patterns. Although existing algorithms can extract most finger-vein patterns robustly, some branch of these patterns always breaks, which leads to adverse effects for features extraction and matching. In this paper, a Direction-Variance-Boundary Constraint Search (DVBCS) model is presented to restore the broken finger-vein patterns. At the beginning, endpoints of broken finger-vein branches are located. Then, a direction constraint for searching candidate point set is demonstrated. Following the second stage, an optimal target point is selected from the candidate point set according to a minimum within-cluster variance criterion. Eventually, the boundary constraint and variance constraint are introduced as the termination conditions. Experimental results illustrate that, while maintaining low segmentation error, the proposed method can restore above 10% lost target points. Moreover, the equal error rate of finger-vein recognition is reduced from 0.57% to 0.29% when using the proposed method to restore finger-vein patterns.

Face retrieval is becoming increasingly useful and important for security maintenance operations. In actual applications, face retrieval is usually influenced by some changeable site conditions, such as various postures, expressions, camera angles, illuminations, and so on. In this paper, facial peculiar features are extracted and classified by dynamically integrated deep neural networks (DNNs), in order to enhance the adaptability in actual conditions. Firstly, eight kinds of facial components are detected and located by clustering analysis and Active Shape Model (ASM). Secondly, certain peculiar patterns are defined for each kind of facial component, and eight specialized DNNs are designed to extract features and classify components. Thirdly, the similarity between faces is calculated by dynamically integrating the results of each DNN. Comparative experiments on standard image sets and wild image sets demonstrate that our algorithm outperforms global feature models in retrieval accuracy. Our algorithm is particularly suitable for practical application with regard to natural real videos and images.

Single Shot Multibox Detector (SSD) provides a powerful framework for detecting objects using a single deep neural network. The detection framework is one of the top object detection algorithms in both accuracy and speed which processes a large set of object locations sampled across an image. However, this framework does not behave well for the task of pedestrian detection since the images in popular pedestrian datasets have multiple objects occlusion problem and contain lots of small objects. In this paper, we incorporate deconvolution and downsampling unit into the SSD framework allowing detection network to recycle feature maps learned from images. The enhanced performance was obtained by changing the structure of classifier network, e.g., by replacing VGGNet with DenseNet. The contribution of this paper is a one-stage approach to compose a single deep neural network for pedestrian detection task in real-time. This approach addresses the typical difficulty of detecting different scale pedestrian at only one layer by providing a novel channel fusion. To solve small objects problem, base network has been replaced with more powerful one. This approach outperforms competing one-single methods on standard Caltech pedestrian dataset benchmark. It is also faster than all the other methods.

The relationships between surface temperature and surface heat-transfer coefficient of different diameter cylinders during quenching have been given. The properties include the coupled effects between phase transformation and temperature, and the coupled effect of the phase transformation between solid and liquid. The comparison of surface heat-transfer coefficients between various diameter cylinders during rapid cooling was taken into account. The results obtained have been shown that the maximum value of the surface heat-transfer coefficient of φ40 mm ×120 mm cylinders is larger than that of φ20 mm ×60 mm.

Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.

Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver’s face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver’s face can be detected rapidly and precisely.

A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.

In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.

In this paper, an algorithm for smoke region of focus (ROF) detection based on surveillance video is proposed in order to solve the problem of limited application in scenes range and imaging environment of the traditional smoke detection algorithm. The frog vision perception model is used in this algorithm. First the suspect regions are detected, and then the static and the dynamic features of the regions are chosen for the smoke ROF detection. Experimental results show that the algorithm is efficient and significant for improving the operational rate of the detection.