Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.

/pdf/detecting-faces-in-images-a-survey-4yhbbns6mr.pdf

Detecting faces in images: a survey

In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.

/pdf/neighbourhood-components-analysis-2fl2gsy4cc.pdf

Neighbourhood Components Analysis

Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

A direct LDA algorithm for high-dimensional data — with application to face recognition

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

Face Detection

https://ir.nctu.edu.tw:443/bitstream/11536/30219/1/000088547100011.pdf

A new LDA-based face recognition system which can solve the small sample size problem

A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

/pdf/a-new-lda-based-face-recognition-system-which-can-solve-the-3orbpyqkqi.pdf

In this paper, we describe the development of a mobile butterfly-watching learning (BWL) system which supports independent learners by offering a new pattern of outdoor mobile learning (or called as m-learning) activities. The proposed BWL system was designed using a wireless mobile ad-hoc learning environment. In our designed system, each individual learner has a wireless handheld device, which is a PDA (personal digital assistant) with an IEEE 802.11 wireless network card and a small-sized CCD camera. One instructor has a notebook computer with a wi-fi wireless LAN card which serves as the local server. The notebook has a complete butterfly database. All learners' wireless handheld devices and the notebook constitute a mobile ad-hoc learning environment. During the butterfly-watching activity, each learner takes a distinct butterfly picture, and wirelessly transmits the picture to the local server. A content-based butterfly-image retrieval technique is applied to search for the most closely matching butterfly information, which is returned in real time to the learner's wireless handheld device. To illustrate the effect of the BWL system, an outdoor BWL activity was actually performed at elementary school in Taiwan. This article reports on our attempts to develop a new m-learning system to support an independent learning model. We deeply believe that it is worth developing a mobile learning system to provide a possible independent-learning environment for outdoor learners.

/pdf/a-mobile-butterfly-watching-learning-system-for-supporting-4rn777xep2.pdf

Gwo-Jong Yu

Papers

A new LDA-based face recognition system which can solve the small sample size problem

A new LDA-based face recognition system which can solve the small sample size problem

A mobile butterfly-watching learning system for supporting independent learning

Fast face detection via morphology-based pre-processing ☆

A real-time network intrusion detection system for large-scale attacks based on an incremental mining approach