Face recognition using a permutation coding neural classifier

Face recognition is an important security task. We propose a high-level method to solve this problem: a permutation coding neural classifier (PCNC). A PCNC with a special feature extractor for face image recognition systems is a relatively new method that has been tested with good results to classify real environment images (such as larvae of various types and handmade elements). As baseline methods, a support vector machine (SVM) and the iterative closest point (ICP) method are selected for comparison. We applied these methods to gray-level images from the FRAV3D face database. Fifteen experiments were performed to examine a large set of training and testing conditions. As a general result, it was observed that errors are lower with the PCNC than with the SVM and the ICP classifier. We aggregated various distortions for the initial images to improve the PCNC. We analyze and discuss the obtained results.     

High performance iris recognition based on 1-D circular feature extraction and PSO–PNN classifier

In this paper, a novel iris feature extraction technique with intelligent classifier is proposed for high performance iris recognition. We use one dimensional circular profile to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1-D wavelet transform. So as to improve the accuracy, this paper combines probabilistic neural network (PNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. A comparative experiment of existing methods for iris recognition is evaluated on CASIA iris image databases. The experimental results reveal the proposed algorithm provides superior performance in iris recognition.     

A neural network based retrainable framework for robust object recognition with application to mobile robotics

In this paper, we address object recognition for a mobile robot which is deployed in a multistory building. To move to another floor, a mobile robot should recognize various objects related to an elevator, e.g., elevator control, call buttons, and LED displays. To this end, we propose a neural network based retrainable framework for object recognition, which consists of four components—preprocessing, binary classification, object identification, and outlier rejection. The binary classifier, a key component of our system, is a neural network that can be retrained, the motivation of which is to adapt to varying environments, especially with illuminations. Without incurring any extra process to prepare new training samples for retraining, they are freely obtained as a result of the outlier rejection component, being extracted on-line. To realize a practical system, we adopt a parallel architecture integrating both recognition and retraining processes for seamless object recognition, and furthermore detect and cope with the deterioration of a retrained neural network to ensure high reliability. We demonstrate the positive effect of retraining on the object recognition performance by conducting experiments over hundreds of images obtained in daytime and nighttime.     

Color refinement using deep neural networks for enhancing color recognition in a projector‐camera system

In projector‐camera systems, object recognition is essential to enable users to interact with physical objects. Among several input features used by the object classifier, color information is widely used as it is easily obtainable. However, the color of an object seen by the camera changes due to the projected light from the projector, which degrades the recognition performance. To solve this problem, we propose a method to restore the original color of an object from the observed color through camera. The color refinement method has been developed based on the deep neural network. The inputs to the neural network are the color of the projector light as well as the observed color of the object in multiple color spaces, including RGB, HSV, HIS, and HSL. The neural network is trained in a supervised manner. Through a number of experiments, we show that our refinement method reduces the difference from the original color and improves the object recognition rate implemented with a number of classification methods.     

Human face recognition based on ensemble of polyharmonic extreme learning machine

This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate.     

基于粗糙集和神经网络的分类器及其在LPR中的应用

粗糙集和神经网络在模式识别中都可用于分类,但是都有局限性。虽然两者没有太多的共同点,将它们结合起来却能相互补充,起到比单个理论更好的分类效果。本文从理论上给出了用粗糙集约简算法减少BP网络中的一个神经元或连接时网络输出能产生的最大误差。接着将粗糙集和BP网络结合起来设计分类器,并通过车牌数字识别验证了该分类器的有效性。实验说明该分类器比单独用粗糙集和神经网络设计的分类器识别率高、识别时间短。     

Speech recognition using hybrid hidden markov model and NN classifier

This paper discusses the use of an integrated HMM/NN classifier for speech recognition. The proposed classifier combines the time normalization property of the HMM classifier with the superior discriminative ability of the neural net (NN) classifier. Speech signals display a strong time varying characteristic. Although the neural net has been successful in many classification problems, its success (compared to HMM) is secondary to HMM in the field of speech recognition. The main reason is the lack of time normalization characteristics of most neural net structures (time-delay neural net is one notable exception but its structure is very complex). In the proposed integrated hybrid HMM/NN classifier, a left-to-right HMM module is used first to segment the observation sequence of every exemplar into a fixed number of states. Subsequently, all the frames belonging to the same state are replaced by one average frame. Thus, every exemplar, irrespective of its time scale variation, is transformed into a fixed number of frames, i.e., a static pattern. The multilayer perceptron (MLP) neural net is then used as the classifier for these time normalized exemplars. Some experimental results using telephone speech databases are presented to demonstrate the potential of this hybrid integrated classifier.     

A robust similarity based deep siamese convolutional neural network for gait recognition across views

Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies.     

基于全卷积神经网络的船舶检测和船牌识别系统

船舶检测与识别对于港口智能监控,实现港口资源的有效管理具有重要意义。由于复杂的船舶轮廓、船牌位置不固定、船牌文本类型复杂多样和船牌文字个数不确定等因素,使得船舶的检测和识别非常具有挑战性。本文提出一种基于全卷积神经网络的船舶检测与识别方法：SDR-FCN。SDR-FCN利用本文提出的船舶检测算法SDNet进行船舶检测定位,然后利用本文提出的船牌文本检测算法PDNet进行船牌文字检测,最后利用具备在线自适应性的分类器OA-Classifier进行船牌分类识别。OA-Classifier综合了AIS（船舶自动识别系统）反馈的信息,提高了分类器的识别精度。实际SDR-FCN部署运行表明,它能够以较高的精度可靠地工作,满足实际应用。     

Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition

The polynomial classifier (PC) that takes the binomial terms of reduced subspace features as inputs has shown superior performance to multilayer neural networks in pattern classification. In this paper, we propose a class-specific feature polynomial classifier (CFPC) that extracts class-specific features from class-specific subspaces, unlike the ordinary PC that uses a class-independent subspace. The CFPC can be viewed as a hybrid of ordinary PC and projection distance method. The class-specific features better separate one class from the others, and the incorporation of class-specific projection distance further improves the separability. The connecting weights of CFPC are efficiently learned class-by-class to minimize the mean square error on training samples. To justify the promise of CFPC, we have conducted experiments of handwritten digit recognition and numeral string recognition on the NIST Special Database 19 (SD19). The digit recognition task was also benchmarked on two standard databases USPS and MNIST. The results show that the performance of CFPC is superior to that of ordinary PC, and is competitive with support vector classifiers (SVCs).     

FPGA realization of the LIRA neural classifier

Different types of neural networks can be used to classify images. We propose to apply LIRA (LImited Receptive Area) neural classifier to work with images. To accelerate the neural network functioning we propose a digital implementation of the LIRA neural classifier. We begin with a neuron design, and then continue with the neural network simulation. The advantage of neural network is its parallel structure and possibility of the training. FPGA (Field Programmable Gate Array) allows the implementation of these parallel algorithms in a single device. Speed of classification is one of the most important requirements in adaptive control systems based on computer vision. The contribution of this article is LIRA neural classifier implementation with FPGA for two classes to accelerate the training and recognition processes.     

Object representation and recognition in shape spaces

In this paper, we describe a shape space based approach for invariant object representation and recognition. In this approach, an object and all its similarity transformed versions are identified with a single point in a high-dimensional manifold called the shape space. Object recognition is achieved by measuring the geodesic distance between an observed object and a model in the shape space. This approach produced promising results in 2D object recognition experiments: it is invariant to similarity transformations and is relatively insensitive to noise and occlusion. Potentially, it can also be used for 3D object recognition.     

Prototype-based minimum error training for speech recognition

A key concept in pattern recognition is that a pattern recognizer should be designed so as to minimize the errors it makes in classifying patterns. In this article, we review a recent, promising approach for minimizing the error rate of a classifier and describe a particular application to a simple, prototype-based speech recognizer. The key idea is to define a smooth, differentiable loss function that incorporates all adaptable classifier parameters and that approximates the actual performance error rate. Gradient descent can then be used to minimize this loss. This approach allows but does not require the use of explicitly probabilistic models. Furthermore, minimum error training does not involve the estimation of probability distributions that are difficult to obtain reliably. This new method has been applied to a variety of pattern recognition problems, with good results. Here we describe a particular application in which a relatively simple distance-based classifier is trained to minimize errors in speech recognition tasks. The loss function is defined so as to reflect errors at the level of the final, grammar-driven recognition output. Thus, minimization of this loss directly optimizes the overall system performance.     

Static hand gesture recognition using neural networks

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.     

A novel method for automatic modulation recognition

Automatic recognition of the digital modulation plays an important role in various applications. This paper investigates the design of an accurate system for recognition of digital modulations. First, it is introduced an efficient pattern recognition system that includes two main modules: the feature extraction module and the classifier module. Feature extraction module extracts a suitable combination of the higher order moments up to eighth, higher order cumulants up to eighth and instantaneous characteristics of digital modulations. These combinations of the features are applied for the first time in this area. In the classifier module, two important classes of supervised classifiers, i.e., multi-layer perceptron (MLP) neural network and hierarchical multi-class support vector machine based classifier are investigated. By experimental study, we choose the best classifier for recognition of the considered modulations. Then, we propose a hybrid heuristic recognition system that an optimization module is added to improve the generalization performance of the classifier. In this module we have used a new optimization algorithm called Bees Algorithm. This module optimizes the classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent technique has very high recognition accuracy even at low levels of SNR with a little number of the features.     

英文字符特征提取系统

英文字符识别是模式识别的一个重要分支,具有广泛的应用领域.字符识别主要包括文档切分、单词切分、字符识别及后处理几部分.文中描述的是英文字符识别系统实现了从图像扫描到得到识别结果的全过程, 而字符特征提取是文本的重点内容.以五十二个英文字符为研究对象,具体包括了图像预处理、特征提取、建立模板、分类器设计、后处理等步骤.文章对OCR领域中应用比较广泛的网格特征、外围特征、穿越特征等特征和几种距离分类器分别进行比较分析,并进行大量的实验.实验结果表明识别准确率和识别处理时间方面具有良好性能.     

Application notes - Algorithms for Assessing the Quality of Facial Images

In this paper, we presented algorithms to assess the quality of facial images affected by factors such as blurriness, lighting conditions, head pose variations, and facial expressions. We developed face recognition prediction functions for images affected by blurriness, lighting conditions, and head pose variations based upon the eigenface technique. We also developed a classifier for images affected by facial expressions to assess their quality for recognition by the eigenface technique. Our experiments using different facial image databases showed that our algorithms are capable of assessing the quality of facial images. These algorithms could be used in a module for facial image quality assessment in a face recognition system. In the future, we will integrate the different measures of image quality to produce a single measure that indicates the overall quality of a face image     

A subclass supported convolutional neural network for object detection and localization in remote-sensing images

This article proposes a novel subclass-based classifier based on convolutional neural networks (CNNs) for detecting objects more accurately on remote-sensing images. The proposed classifier, called subclass supported CNN (SSCNN), is used to separate the representation of the objects into subclasses such as nearcentre, centre, and border depending on the distance of the object centre to obtain more effective feature extractor. A three-stage object recognition framework is used to evaluate the performance of the proposed classifier. In the first of these stages, the Selective Search algorithm generates object proposals from the image. Then, the proposed SSCNN classifies the proposals. Finally, subclass-based localization evaluation function has been proposed to calculate the localization of the object with classification results. Due to the limited number of satellite image samples, pretrained AlexNet is used by transfer learning approach to build effective feature extractor. The proposed method has been compared with region-based CNN (R-CNN) on a four-class remote-sensing test dataset consisting of 411 airplanes, 240 baseball diamonds, 468 storage tanks, and 83 ground track fields. In addition, Faster R-CNN has been trained with SSCNN features and the performances of the trained Faster R-CNNs are comparatively evaluated on 10-class remote-sensing image dataset. Experiment results have shown that the proposed framework can locate the objects precisely.