The first thing to say about this book is that I love the cover. It is such a great illustration of how our facial features, though changing with the passage of time, are a unique identifier of who we are. This cover should convince those who are sceptical of mothers' abilities to recognise their own child after separations of sometimes many years.

The book is very technical and gives a comprehensive overview of the development of the face and the various conditions caused when this process goes wrong. Although the ‘blurb’ inside the jacket mentions that it would be of interest to, amongst others, sociologists and art historians I suspect that its technical detail makes it of more interest to those involved in dealing with abnormalities of the face such as craniofacial surgeons, plastic surgeons, dentists and dysmorphologists. I am not sure that the detailed lists of the many genes expressed in craniofacial development will be of interest to non geneticists. I have to say I found the section on the evolutionary perspective quite hard going and felt more comfortable when we got on to the embryonic and developmental aspects. There are some nice descriptions and diagrams of genetic interactions and some very good 3D scan images.

Although there are bits in the sections on psychological, sociocultural and artistic perspectives that were of interest to me, they didn't seem to me to sit well next to the very technical sections on, for example, the surgical perspective. I almost had the feeling of there being two books with sections from each being interspersed.

Michael Cohen obviously has a wonderful memory for unusual facts and the book is peppered with these little gems. For me it was news to know that someone with typhoid fever is said to have a body odour akin to freshly baked brown bread and someone with typhus, one like a butcher's shop.

In summary there is a huge amount of information in this book, much of which is very interesting. However I am not sure that it entirely works as a coherent whole.

Perspectives on the Face

In this paper, a face recognition fusion algorithm, namely WT-LLE-LSSVM, based on wavelet transform (WT) and local linear embedding (LLE) is proposed. Firstly, the face image is pre-processed, and decomposed by Wavelet Transform to obtain four components of face image; then, the LLE algorithm is carried out to extract the features from the four components respectively, and the weighted fusion is used to obtain the face recognition feature vector; Finally, least squares support vector machine (LSSVM) is conducted to learn the training samples with feature vector reduction, and the classifier of face recognition is established. In the case of different pixels, three face databases are chosen to test some main performance indexes such as recognition rate, speed and time of the algorithm. The simulation results show that compared with the traditional face recognition algorithm, the proposed algorithm can improve the face recognition rate, as well as the computing rate, and enhance the efficiency of face recognition.

Face recognition algorithm based on wavelet transform and local linear embedding

Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.

/pdf/face-recognition-system-based-on-principal-components-3yh9hjlcw2.pdf

Face recognition system based on principal components analysis and distance measures

In pattern recognition and computer vision the face recognition has been an important issue. Face recognition has many challenges like illumination variations ,pose variations, facial expressions, occlusion and aging. This paper presents a model for unconstrained face recognition by considering the pose variation and illumination variation. The texture features like Haralick features and log Gabor features are to be extracted. The recognition is to be performed using Support Vector Machine Classifier. The model will provide better accuracy of the recognition with various in the facial pose.

/pdf/a-model-for-unconstrained-face-recognition-system-3ep09xlwwf.pdf

A Model for Unconstrained Face Recognition System

- Face recognition has been a central research topic in recent years. Because it has many different application areas in automatic surveillance systems, passport control, missing person detection and criminal detection, many researchers have studied the problems of face recognition and authentication for many years. However, still, many issues remain to be addressed. The limitation of this topic includes the variability in poses, facial expressions, illumination, and a large age gap between the face images. This paper presents the survey for some commonly used techniques for Age Invariant face recognition (AIFR). A comprehensive overview of the most used age-invariant methods in face recognition is presented, with a comparative overview of different approaches and a comparison in accuracy. Some facial ageing datasets, with their essential characteristics in images, no. of subjects, and age gap, are briefly presented in a tabular view. Commonly used techniques for feature extraction and classification in this field are presented. Major topics covered are experimental results obtained from these methods, issues/challenges, the scope of future work, and conclusions.

Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions

An embedded system is presented in which face recognition and facial recognition for Human-Robot Interaction are implemented. To detect face with a fast and reliable way, HMM combined with SVM algorithm is used. The full pose from face recognition data base is considered to detect the face recognition. Performance of the face recognition reaches to 99.617%. The two main advantages of our method are that it does not require manually selected facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, an algorithm is presented for classifying whether a given face image is at a frontal or non frontal pose. Experimental results on different datasets are presented to demonstrate the effectiveness of the proposed approach. In addition to the proposed method, a pre processing state of detecting the face is included here. The images which are non faces are detected and eliminated from the database is an another main advantage in of this work.

Pose invariant face recognition using HMM and SVM using PCA for dimensionality reduction

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Pose invariant face recognition using HMM and SVM using PCA for dimensionality reduction