基于肤色特征和Adaboost算法的人脸检测方法探析

针对采用基于肤色特征的人脸检测方法和基于Ada Boost算法的人脸检测,单一方法的人脸检测系统在检测率和误检率方面不能同时达到比较好的效果。因此结合上述两种算法各自的优点,将两种方法相结合并加以改进,主要思想是基于肤色特征的人脸检测作为预人脸检测,得到含有人脸的肤色区域,运用级联分类器检测这些肤色区域。利用matlab仿真软件进行了大量的仿真探析并进行了统计与分析,探析表明改进算法在误检率和检测率方面明显优于两种单独算法,同时对于人脸姿势方面,也能够达到很好的检测效果。     

Single face hallucination via local neighbor patches

Based on learning neighborhood patches a new single face hallucination method is proposed in this paper. In the proposed method, each input low-resolution (LR) position-patch and all patches in a local window centered at the same position of training images are used to hallucinate a high-resolution (HR) face patch, meanwhile two local similarity measurements between each input LR patch and all local LR and HR neighborhood patches of training images are computed to constrain the hallucination. Additionally, a residue image is estimated for the further improvement of the reconstructed result. Experimental results show that the proposed method can obtain superior or competitive results.     

结合肤色分割的支持向量机人脸检测方法

人脸检测是人脸识别与图像及视频检索的一项重要任务。本文提出了一种结合分割与模板匹配-支持向理机(Support Vector Machines,SVM)的正面人脸检测算法。算法在肤色区域分割基础上，输入图像采用基于Harr小波描述的特征提取方法，然后结合平均脸模板匹配利用 SVM分类器进行分类。实验结果表明该算法十分有效。     

Design of a real-time face detection architecture for heterogeneous systems-on-chips

Object detection represents one of the most important and challenging task in computer vision applications. Boosting-based approaches deal with computational intensive operations and they involve several sequential tasks that make very difficult developing hardware implementations with high parallelism level. This work presents a new hardware architecture able to perform object detection based on a cascade classifier in real-time and resource-constrained systems. As case study, the proposed architecture has been tailored to accomplish the face detection task and integrated within a complete heterogeneous embedded system based on a Xilinx Zynq-7000 FPGA-based System-on-Chip. Experimental results show that, thanks to the proposed parallel processing scheme and the runtime adaptable strategy to slide sub-windows across the input image, the novel design achieves a frame rate up to 125fps for the QVGA resolution, thus significantly outperforming previous works. Such a performance is obtained by using less than 10% of on-chip available logic resources with a power consumption of 377  mW at the 100 MHz clock frequency.     

一种稳健的人脸检测算法

人脸检测是人脸识别系统的重要组成部分,对于安全级别较高或特殊场合的门禁系统而言,高准确率的人脸识别技术尤为重要。为提高门禁系统的安全性,采用了多种特征相结合的人脸识别算法,融合了背景分离、肤色检测、人脸五官特征检测、运动物体轮廓分析、人体运动跟踪等多种技术进行人脸检测测试,有效地解决了单一特征的人脸检测方法对人脸进行漏检和误检的问题。实验结果表明,该算法在复杂背景和光照条件不足以及有遮蔽物的情况下,均能快速准确地检测出人脸,误检率低。     

基于特征脸空间重建的彩色人脸检测方法

提出的人脸检测方法利用人类肤色在色度空间分布的稳定性，检测出图像中的皮肤区域，然后将其在特征脸空间中投影，重建，通过求重建图像的信噪比进行判断。实验结果证明了方法的有效性。     

基于网络事件和深度协议分析的入侵检测研究

针对制约NIDS(基于网络的入侵检测系统)的问题,提出了基于网络事件和深度协议分析的入侵检测模型MIDM,实现了对入侵的分析与综合。扩展了ABNF范式形式化定义网络事件,基于所提出模型重新实现了入侵检测系统。实验证明与当前主流NIDS相比,新模型有效降低了误检率和特征库冗余;具有随网络流量和特征库快速增长,而CPU占用率维持低水平增长的特性,能更好地适应高速网络环境;同时还具有一定的特征泛化和检测未知入侵的能力。     

一种动态视频流实时多人脸检测算法

针对传统的AdaBoost人脸检测算法对侧面及多姿态人脸检测误检率较高且检测速度较慢等问题,提出了一种动态视频流实时多人脸检测算法。仿真实验表明,通过使用该算法对静态图像以及动态视频流中的多个人脸实时检测,其结果比传统算法具有更低的误检率和更快检测速度。     

Robust skin detection in real-world images

Human skin detection in images is desirable in many practical applications, e.g., human–computer interaction and adult-content filtering. However, existing methods are mainly suffer from confusing backgrounds in real-world images. In this paper, we try to address this issue by exploring and combining several human skin properties, i.e. color property, texture property and region property. First, images are divided into superpixels, and robust skin seeds and background seeds are acquired through color property and texture property of skin. Then we combining color, region and texture properties of skin by proposing a novel skin color and texture based graph cuts (SCTGC) to acquire the final skin detection results. Comprehensive and comparative experiments show that the proposed method achieves promising performance and outperforms many state-of-the-art methods over publicly available challenging datasets with a great part of hard images.     

Color image canonical correlation analysis for face feature extraction and recognition

Canonical correlation analysis (CCA) is a powerful statistical analysis technique, which can extract canonical correlated features from two data sets. However, it cannot be directly used for color images that are usually represented by three data sets, i.e., red, green and blue components. Current multi-set CCA (mCCA) methods, on the other hand, can only provide the iterative solutions, not the analytical solutions, when processing multiple data sets. In this paper, we develop the CCA technique and propose a color image CCA (CICCA) approach, which can extract canonical correlated features from three color components and provide the analytical solution. We show the mathematical model of CICCA, prove that CICCA can be cast as solving three eigen-equations, and present the realization algorithm of CICCA. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that CICCA outperforms several representative color face recognition methods.     

基于层次模型和融合决策的多姿态人脸识别技术

该文提出了基于层次模型和融合决策的多姿态人脸识别技术,它首先把各人脸按姿态分成几个大类,并且对各大类按人脸个体分成相应子类,然后对各个大类分别进行基于特征脸的人脸识别,最后对各个姿态的人脸识别中间结果进行融合决策得到真正的人脸识别结果,该算法同时也提供了其姿态识别结果,并且大大减小了耗时,该文算法对ORL,UMIST,Stirling数据和一些自拍数据共1200幅人脸图像进行了识别测试实验,其结果令人鼓舞。     

Local appearance based face recognition method using block based steerable pyramid transform

In this paper, an efficient local appearance feature extraction method based on Steerable Pyramid (S-P) wavelet transform is proposed for face recognition. Local information is extracted by computing the statistics of each sub-block obtained by dividing S-P sub-bands. The obtained local features of each sub-band are combined at the feature and decision level to enhance face recognition performance. The purpose of this paper is to explore the usefulness of S-P as feature extraction method for face recognition. The proposed approach is compared with some related feature extraction methods such as principal component analysis (PCA), as well as linear discriminant analysis LDA and boosted LDA. Different multi-resolution transforms, wavelet (DWT), gabor, curvelet and contourlet, are also compared against the block-based S-P method. Experimental results on ORL, Yale, Essex and FERET face databases convince us that the proposed method provides a better representation of the class information, and obtains much higher recognition accuracies in real-world situations including changes in pose, expression and illumination.     

Visible-light and near-infrared face recognition at a distance

A method to solve the problem of face recognition at a distance (FRAD) under the visible-light (VIS) and the near-infrared (NIR) spectra is presented in this work. For images taken under visible light at day time, we perform the coarse-scale alignment/enhancement to eliminate a set of unlikely candidates at the first stage. Then, the fine-scale alignment/enhancement steps are conducted to refine the candidate list furthermore iteratively at the second stage. To address the additional challenge associated with NIR images captured at night time, we incorporate a restoration mechanism that reconstructs low-quality patches through a locally linear embedding (LLE) process with a local constraint. It is shown by experimental results that our FRAD solution outperforms state-of-the-art methods on both VIS and NIR images.     

Saliency model-based face segmentation and tracking in head-and-shoulder video sequences

In this paper, a novel face segmentation algorithm is proposed based on facial saliency map (FSM) for head-and-shoulder type video application. This method consists of three stages. The first stage is to generate the saliency map of input video image by our proposed facial attention model. In the second stage, a geometric model and an eye-map built from chrominance components are employed to localize the face region according to the saliency map. The third stage involves the adaptive boundary correction and the final face contour extraction. Based on the segmented result, an effective boundary saliency map (BSM) is then constructed, and applied for the tracking based segmentation of the successive frames. Experimental evaluation on test sequences shows that the proposed method is capable of segmenting the face area quite effectively.     

Interoperable vision component for object detection and 3D pose estimation for modularized robot control

Finding objects and tracking their poses are essential functions for service robots, in order to manipulate objects and interact with humans. We present novel algorithms for local feature matching for object detection, and 3D pose estimation. Our feature matching algorithm takes advantage of local geometric consistency for better performance, and the new 3D pose estimation algorithm solves the pose in a closed-form using homography, followed by a non-linear optimization step for stability. Advantages of our approach include better performance, minimal prior knowledge for the target pattern, and easy implementation and portability as a modularized software component. We have implemented our approach along with both CPU and GPU-based feature extraction, and built an interoperable component that can be used in any Robot Technology (RT)-based control system. Experiment shows that our approach produces very robust results for the estimated 3D pose, and maintain very low false positive rate. It is also fast enough to be used in on-line applications. We integrated our vision component in an autonomous robot system with a search-and-grasp task, and tested it with several objects that are found in ordinary domestic environment. We present the details of our approach, the design of our modular component design, and the results of the experiments in this paper.     

Face presentation attack detection based on chromatic co-occurrence of local binary pattern and ensemble learning

To counter face presentation attacks in face recognition (FR), color texture has been successfully used for face presentation attack detection (PAD) in recent years. However, the existing research does not fully consider the correlation between different color channels as well as the optimization of classification for face PAD. To resolve these limitations, a face PAD scheme based on chromatic co-occurrence of local binary pattern (CCoLBP) and ensemble learning (EL) is proposed in this paper. A color distortion-based face PAD model is first built, and then the chromatic discrepancies between bona fide faces and artefacts are analyzed. After that, CCoLBP is extracted as the feature to characterize these discrepancies. Meanwhile, an EL based classifier is put forward to reduce the effect of class imbalance and to improve the generalization ability. Experimental results and analysis indicate that the proposed scheme can achieve an overall good performance. Moreover, it can achieve significant improvement in the cross-database test, and its computational complexity can meet the requirement of real time applications.     

基于视频的人脸检测综述

在实时系统中视频人脸的检测与跟踪技术已成为人脸识别领域最活跃的研究方向之一,介绍了人脸检测的应用,对提高检测和跟踪的鲁棒性和精确性问题的理论研究算法进行了总结。分别讨论并评价了人脸检测的几种基本方法,介绍了近年来研究者们研究的新方法并对人脸检测研究中存在的问题和今后的发展进行了讨论。