http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

https://www.cin.ufpe.br/~rps/Artigos/Face%20Detection%20-%20%20A%20Survey.pdf

Face Detection

A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graph-based techniques in the Pattern Recognition and Machine Vision field.

/pdf/thirty-years-of-graph-matching-in-pattern-recognition-58lvpdkcaj.pdf

Thirty years of graph matching in pattern recognition

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

An extension of the bipartite weighted matching problem

A new approach to stroke-order and stroke-number free on-line handwritten Chinese character recognition is presented in this paper. In this new scheme, the decision rule of the segment attribute is used to characterize the segment sequence appearing in each Chinese character for recognizing connected-stroke and even cursive handwritten Chinese characters. A knowledge-based radical extraction method is proposed to perform the feature extraction before radical recognition stage. The top-level and bottom-level radical classification are adopted in the coarse classification stage to reduce the number of candidate characters. In order to develop a stroke order free system, the neighboring segment matching method is proposed. Experimental results show that the proposed scheme is an efficient solution for stroke-order and stroke-number free on-line Chinese character recognition. The recognition rate is 93.4% and the recognition speed is 0.6 second per character.

Radical-based neighboring segment matching method for on-line Chinese character recognition

A robust and efficient human face detection system that can detect multiple faces in various surroundings is presented in this paper. The proposed system consists of two principal parts. The first part is to search for the potential face regions. The second part is to perform face verification task. Our system can conquer different size, different lighting condition, varying pose and expression, and noise and defocus problems. In addition to overcome the problem of partial occlusion of mouth and sunglasses, the color segmentation process can successfully extract skin color from complex backgrounds. Experimental results demonstrate that an approximately 98 % success rate is achieved. The experimental results reveal that the proposed method is better than traditional methods in terms of efficiency and accuracy.

A Color-Triangle-Based Approach to the Detection of Human Face

Illumination and expression variations are still a challenging problem in face recognition. In this work, we present an efficient face recognition method which can solve the above two problems with single training sample. At first, the effect of the lighting variation is effectively eliminated by the Mutil-Scale Retinex algorithm. The Active Appearance Model is adopted to extract the facial block feature to establish the component-based face recognition system. Different from other methods which construct the various classifiers corresponding to the specific facial expression, the proposed method decreases the weights of some dominated facial features which are affected by the severe facial expression. By learning a block weighting support vector machine, the component based approach is achieved. The proposed algorithm has two advantages: (1) only single one face training image is needed to train the classifier; (2) by using the facial block features with lower data dimensions, the proposed system is more computational efficiency. In particular, the proposed method achieves 97.94% face recognition accuracy when only using one training sample on the Yale B database. Experimental results demonstrate that the proposed method has reliable recognition rate when face images are under illumination and facial expression variations.

Robust face recognition under illumination and facial expression variations

In this paper, a novel local pattern descriptor generated by the proposed local vector pattern (LVP) in high-order derivative space is presented for face recognition. The proposed vector representation of the referenced pixel is generated to provide the one-dimensional structure of micropatterns. To effectively extract more detailed discriminative information in a given sub-region, the vector of LVP is refined by varying local derivative directions from the nth-order LVP in (n−1)th-order derivative space. The proposed LVP is compared with the existing local pattern descriptors including local binary pattern (LBP), local derivative pattern (LDP), and local tetra pattern (LTrP) to evaluate the performances from input grayscale face images. Extensive experiments conducting on benchmark face image databases, FERET and Extended Yale B, demonstrate that the proposed LVP in high-order derivative space indeed performs much better than LBP, LDP and LTrP for face recognition.

Kuo-Chin Fan

Papers

An extension of the bipartite weighted matching problem

Radical-based neighboring segment matching method for on-line Chinese character recognition

A Color-Triangle-Based Approach to the Detection of Human Face

Robust face recognition under illumination and facial expression variations

Local vector pattern in high-order derivative space for face recognition