Hand shape recognition based on coherent distance shape contexts

In this paper, we propose a novel hand shape recognition method named as Coherent Distance Shape Contexts (CDSC), which is based on two classical shape representations, i.e., Shape Contexts (SC) and Inner-distance Shape Contexts (IDSC). CDSC has good ability to capture discriminative features from hand shape and can well deal with the inexact correspondence problem of hand landmark points. Particularly, it can extract features mainly from the contour of fingers. Thus, it is very robust to different hand poses or elastic deformations of finger valleys. In order to verify the effectiveness of CDSC, we create a new hand image database containing 4000 grayscale left hand images of 200 subjects, on which CDSC has achieved the accurate identification rate of 99.60% for identification and the Equal Error Rate of 0.9% for verification, which are comparable with the state-of-the-art hand shape recognition methods.     

Palmprint based recognition system using phase-difference information

This paper presents an efficient palmprint based human recognition system. Each palmprint is divided into several square overlapping blocks. Reconstruction error using principle component analysis (PCA) is used to classify these blocks into either a good block or a non-palmprint block. Features from each good block of a palmprint are obtained by binarising the phase-difference of vertical and horizontal phase. The Hamming distance is used to compute the matching score between the features of corresponding good blocks of enrolled and live palmprint. These matching scores are fused using weighted sum rule, where weights are based on the average discriminating level of a block relative to other blocks. The performance of the proposed system is analysed on different datasets of hand images and it has been observed that it achieves a Correct Recognition Rate of 100% with a low Equal Error Rate for all the datasets. The system is also evaluated for noisy and bad palmprint images. It is found to be robust as long as the noise density is less than 50% or the bad region is less than 64% of the images.     

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.     

Integrated features and GMM Based Hand Detector Applied to Character Recognition System under Practical Conditions

Detection of bare-hand under non-ideal conditions is a challenging task. Most of the existing hand detection systems are developed under limited environmental constraints. In this study, a robust two-level bare-hand detector is integrated with a 58 keyboard characters recognition model. At first, the Gaussian mixture model (GMM) based foreground detector is used to segment the region of interest (ROI), which is further classified using Color-texture and texture based models to detect the actual fist. The detected hand is tracked using modified Kanade–Lucas–Tomasi (KLT) tracker to generate the required trajectory points of the character. The feature space for character recognition consists of existing features and three new features, namely, Local Geometrical Area Ratio (LGAR), Area of two halves (ATH), Curve-Area feature (CAF) that are extracted from the trajectory points. Feature space is optimized using statistical analysis algorithms. Multi-factor analysis of individual character subsets such as alphabets, numbers, ASCII characters, etc., are carried out using multiple conventional classifiers along with Support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and proposed Neuro-fuzzy classifiers. The proposed GMM based motion detection method achieves an accuracy of 100% during the segmentation of ROI, followed by an increase of 46.77% in the accuracy of two-level hand detection under non-ideal conditions. Maximum accuracy of 58 character system using proposed features and ANN classifier is observed to be 92.56%.

     

Features extraction from hand images based on new detection operators

Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed.     

Autonomous Navigation for Multiple Mobile Robots under Time Delay in Communication

To facilitate full-loaded commandos to control reconnaissance robots, in this paper, we propose a wearable hand posture control system based on egocentric-vision by imitating the sign language interaction way among commandos. Considering the characteristics of the egocentric-vision on the battlefield, such as complicated backgrounds, large ego-motions and extreme transitions in lighting, a new hand detector based on Binary Edge HOG Block (BEHB) features is proposed to extract articulated postures from the egocentric-vision. Different from many other methods that use skin color cues, our proposed hand detector adopts contour cues and part-based voting idea. This means that our algorithm can be used on the battlefield even in dark environment, because infrared cameras can be used to get contour images rather than skin color images. The experiment result shows that the proposed hand detector can get a better posture detection result on the NUS hand posture dataset II. To improve hand recognition accuracy, a deep ensemble hybrid classifier is proposed by combing hybrid CNN-SVM classifier and ensemble technique. Compared with other state-of-art algorithms, the proposed classifier yields a recognition accuracy of 97.72 % on the NUS hand posture dataset II. At last, to reduce misjudgments during consecutive posture switches, a vote filter is proposed and applied to the sequence of the recognition results. The scout experiment shows that our wearable hand posture control system is more suitable than traditional hand-held controllers for full-loaded commandos to control reconnaissance robots.     

多特征量对数回归的火焰快速识别算法

为了提高实时视频监控中火焰识别率和降低误识率,提出了一种基于多特征量对数回归模型的火焰快速识别算法。首先,根据火焰的色度特征进行图像分割,通过运动目标与参考图像差分运算获取火焰候选区域（CFR）;然后提取候选区域的面积变化率、圆形度、尖角个数以及质心位移等特征量,建立火焰的对数回归快速识别模型;其次采用美国国家标准与技术研究院（NIST）、仁荷大学计算机视觉实验室（ICV）和基于计算机视觉的火灾探测（VisiFire）实验库以及自制蜡烛、纸燃烧火焰中的火焰和非火焰图像中的300幅进行参数学习;最后选取实验数据库中8段视频共11071幅图像进行识别算法检验。测试结果表明,所提算法的真正率（TPR）达到93%、真负率（TNR）达到98%,识别平均用时0.058 s/帧。所提算法识别速度快且识别率高,可以应用于嵌入式实时图像火焰识别。     

基于多特征i-vector的短语音说话人识别算法

当测试语音时长充足时,单一特征的信息量和区分性足够完成说话人识别任务,但是在测试语音很短的情况下,语音信号里缺乏充分的说话人信息,使得说话人识别性能急剧下降。针对短语音条件下的说话人信息不足的问题,提出一种基于多特征i-vector的短语音说话人识别算法。该算法首先提取不同的声学特征向量组合成一个高维特征向量,然后利用主成分分析（PCA）去除高维特征向量的相关性,使特征之间正交化,最后采用线性判别分析（LDA）挑选出最具区分性的特征,并且在一定程度上降低空间维度,从而实现更好的说话人识别性能。结合TIMIT语料库进行实验,同一时长的短语音（2 s）条件下,所提算法比基于i-vector的单一的梅尔频率倒谱系数（MFCC）、线性预测倒谱系数（LPCC）、感知对数面积比系数（PLAR）特征系统在等错误率（EER）上分别有相对72.16%、69.47%和73.62%的下降。不同时长的短语音条件下,所提算法比基于i-vector的单一特征系统在EER和检测代价函数（DCF）上大致都有50%的降低。基于以上两种实验的结果充分表明了所提算法在短语音说话人识别系统中可以充分提取说话人的个性信息,有利地提高说话人识别性能。     

Static and dynamic information derived from source and system features for person recognition from humming

In this paper, hum of a person (instead of normal speech) is used to design a voice biometric system for person recognition. In addition, a recently proposed static feature set, viz., Variable length Teager energy based Mel Frequency Cepstral Coefficients (VTMFCC), is found to capture source-like information of a hum signal. Effectiveness of VTMFCC over linear prediction (LP) residual to capture the complementary information than MFCC is demonstrated in a hum signal. Person recognition performance is found to be better when a score-level fusion is used by combining evidences from static and dynamic features for MFCC (system) and VTMFCC (source-like) features than MFCC alone. Experiments are validated on two types of dynamic features, viz., delta cepstrum and shifted delta cepstrum. In addition, for score-level fusion using static and dynamic features % identification rate and % Equal Error Rate are observed to outperform by 7.9?% and 0.27?%, respectively than MFCC alone. Furthermore, we have observed that person recognition system gives better performance for larger frame duration 69.6?ms as opposed to traditional 10–30?ms frame duration.     

适用不同Brunnstrom等级患者基于表面肌电信号的动作识别方法

针对不同患病程度的脑卒中患者运动意图识别率低的问题,提出了一种适用于不同Brunnstrom等级患者基于表面肌电信号（sEMG）的动作识别方法．首先将所有等级患者sEMG数据进行融合,使用tsfresh库提取特征,然后基于随机森林（random forest,RF）模型筛选特征,并利用筛选的特征训练动作分类模型．进一步,通过研究动作和康复等级的关系,确定了康复评估动作并设计了康复等级自动评估算法．为了验证所提方法的有效性,在24例患者sEMG数据上进行了测试,实验结果表明所提方法能够将9种动作和6类康复等级的平均识别精度分别提升至89.81%和94%．基于所提方法构建的手部康复机器人系统能够实现康复等级自动评估．     

基于Kinect的指尖检测与手势识别方法

针对基于视频的弯曲指尖点识别难、识别率不高的问题,提出一种基于深度信息、骨骼信息和彩色信息的手势识别方法。该方法首先利用Kinect相机的深度信息和骨骼信息初步快速判定手势在彩色图像中所在的区域,在该区域运用YCrCb肤色模型分割出手势区域;然后计算手势轮廓点到掌心点的距离并生成距离曲线,设定曲线波峰与波谷的比值参数来判定指尖点;最后结合弯曲指尖点特征和最大内轮廓面积特征识别出常用的12个手势。实验结果验证阶段邀请了6位实验者在相对稳定的光照环境条件下来验证提出的方法,每个手势被实验120次,12种手势的平均识别率达到了97.92%。实验结果表明,该方法能快速定位手势并准确地识别出常用的12种手势,且识别率较高。     

A strategic approach to recognize the speech of the children with hearing impairment: different sets of features and models

The automatic speech recognition system is developed and tested for recognizing the speeches of a normal person in various languages. This paper mainly emphasizes the need for the development of a more challenging speaker independent speech recognition system for hearing impaired to recognize the speeches uttered by any Hearing Impaired (HI) speaker. In this work, Gamma tone energy features with filters spaced an equivalent rectangular bandwidth (ERB), MEL & BARK scale, and MFPLPC features are used at the front end and vector quantization (VQ) & multivariate hidden Markov models (MHMM) at the back end for recognizing the speeches uttered by any hearing impaired speaker. Performance of the system is compared for the three modeling techniques VQ, FCM (Fuzzy C means) clustering and MHMM for the recognition of isolated digits and simple continuous sentences in Tamil. Recognition accuracy (RA) is 81.5% with speeches of eight speakers considered for training and speeches of the remaining two speakers considered for testing for speaker independent isolated digit recognition system. Accuracy is found to be 91% and 87.5% for considering 90% of the data for training and 10% for testing for speaker independent isolated digit and continuous speech recognition systems respectively. Accuracy can be further enhanced by having an extensive database for creating models/templates. Receiver operating characteristics (ROC) drawn between True Positive Rate and False Positive Rate is used to assess the performance of the system for HI. This system can be utilized to understand the speech uttered by any hearing impaired speaker and the system facilitates the provision of necessary assistance to them. It ultimately improves the social status of the hearing impaired people and their confidence level will be enhanced.

     

基于S EMG的人手手内动作识别

提出一种基于SEMG的人手手内动作识别系统对十种自定义的人手手内操作进行识别。结合人手操作的常用动作,设计包括平移、转移和旋转等在内的十种人手动作集;采用经验模态分解(EMD)算法对原始肌电信号进行预处理;采用最大Lyapunov指数(MLE)法对经过降噪处理后的肌电信号进行特征提取;将获得的非线性MLE特征通过随机森林算法进行分类,并同其他经典算法进行比较。实验结果表明,该系统可以有效地识别10种不同的人手手内动作,准确率高达91.67%。     

An efficient 3D face recognition approach using local geometrical signatures

This paper presents a computationally efficient 3D face recognition system based on a novel facial signature called Angular Radial Signature (ARS) which is extracted from the semi-rigid region of the face. Kernel Principal Component Analysis (KPCA) is then used to extract the mid-level features from the extracted ARSs to improve the discriminative power. The mid-level features are then concatenated into a single feature vector and fed into a Support Vector Machine (SVM) to perform face recognition. The proposed approach addresses the expression variation problem by using facial scans with various expressions of different individuals for training. We conducted a number of experiments on the Face Recognition Grand Challenge (FRGC v2.0) and the 3D track of Shape Retrieval Contest (SHREC 2008) datasets, and a superior recognition performance has been achieved. Our experimental results show that the proposed system achieves very high Verification Rates (VRs) of 97.8% and 88.5% at a 0.1% False Acceptance Rate (FAR) for the “neutral vs. nonneutral” experiments on the FRGC v2.0 and the SHREC 2008 datasets respectively, and 96.7% for the ROC III experiment of the FRGC v2.0 dataset. Our experiments also demonstrate the computational efficiency of the proposed approach.     

基于RGBD数据的静态手势识别

提出一种基于RGBD数据的手势识别方法,首先采用融合深度信息和彩色信息的手势分割算法分割出手势区域;其次提取静态手势轮廓的圆形度、凸包点及凸缺陷点、7Hu矩特征组成特征向量;最后采用SVM进行静态手势识别。实验结果表明,该方法能有效地识别预定义的5种静态手势,且对环境的适应性比较强。     

Artificial recognition system for defective types of transformers by acoustic emission

An artificial recognition system of defective types for epoxy-resin transformers through acoustic emission (AE) from partial discharge (PD) experiment is proposed. PD detection is an efficient diagnosis method to prevent the failure of electric equipments arising from degrading insulation. However, most of the PD detection methods could be performed only at the shutdown period of equipments. By using AE, the online and real-time detection with defective types could be easily reached. Therefore, in this paper a series of high voltage tests were conducted on pre-faulty transformers to collect the AE signals for recognition system needed. The selected AE features instead of waveform are then extracted from these experimental AE signals for the input characteristic of recognition system. According to these features, effective identification of their defective types can be done using the proposed recognition system that combined particle swarm optimization with an artificial neural network. To demonstrate the effectiveness and feasibility of the proposed approach, the artificial recognition system is applied on both noisy and noiseless circumstances. The experiment showed encouraging results that even with 30% noise per discharge count, an 80% successful recognition rate can still be achieved.     

基于SIFT的说话人唇动识别

针对唇部特征提取维度过高以及对尺度空间敏感的问题,提出了一种基于尺度不变特征变换（SIFT）算法作特征提取来进行说话人身份认证的技术。首先,提出了一种简单的视频帧图片规整算法,将不同长度的唇动视频规整到同一的长度,提取出具有代表性的唇动图片;然后,提出一种在SIFT关键点的基础上,进行纹理和运动特征的提取算法,并经过主成分分析（PCA）算法的整合,最终得到具有代表性的唇动特征进行认证;最后,根据所得到的特征,提出了一种简单的分类算法。实验结果显示,和常见的局部二元模式（LBP）特征和方向梯度直方图（HOG）特征相比较,该特征提取算法的错误接受率（FAR）和错误拒绝率（FRR）表现更佳。说明整个说话人唇动特征识别算法是有效的,能够得到较为理想的结果。     

基于MAP+CMLLR的说话人识别中发声力度问题

为了改善发声力度对说话人识别系统性能的影响,在训练语音存在少量耳语、高喊语音数据的前提下,提出了使用最大后验概率（MAP）和约束最大似然线性回归（CMLLR）相结合的方法来更新说话人模型、投影转换说话人特征。其中,MAP自适应方法用于对正常语音训练的说话人模型进行更新,而CMLLR特征空间投影方法则用来投影转换耳语、高喊测试语音的特征,从而改善训练语音与测试语音的失配问题。实验结果显示,采用MAP+CMLLR方法时,说话人识别系统等错误率（EER）明显降低,与基线系统、最大后验概率（MAP）自适应方法、最大似然线性回归（MLLR）模型投影方法和约束最大似然线性回归（CMLLR）特征空间投影方法相比,MAP+CMLLR方法的平均等错率分别降低了75.3%、3.5%、72%和70.9%。实验结果表明,所提出方法削弱了发声力度对说话人区分性的影响,使说话人识别系统对于发声力度变化更加鲁棒。