Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a...

The Scale Invariant Feature Transform (SIFT) proposed by David G. Lowe has been used in face recognition and proved to perform well. Recently, a new detector and descriptor, named Speed-Up Robust Features (SURF) suggested by Herbert Bay, attracts people’s attentions. SURF is a scale and in-plane rotation invariant detector and descriptor with comparable or even better performance with SIFT. Bec...

Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition. Moreover, recent research has shown that the face images reside on a possibly nonlinear manifold. Thus, how to effectively exploit the hidden structure is a k...

The.authors.present.a.face.recognition.scheme.based.on.semantic.features’.extraction.from.faces.and. tensor.subspace.analysis..These.semantic.features.consist.of.eyes.and.mouth,.plus.the.region.outlined.by. three.weight.centres.of.the.edges.of.these.features..The.extracted.features.are.compared.over.images.in. tensor.subspace.domain..Singular.value.decomposition.is.used.to.solve.the.eigenvalue....

In this paper, we present an ordinal feature based method for face recognition. Ordinal features are used to represent faces. Hamming distance of many local sub-windows is computed to evaluate differences of two ordinal faces. AdaBoost learning is finally applied to select most effective hamming distance based weak classifiers and build a powerful classifier. Experiments demonstrate good result...

Face recognition has many important practical applications, like surveillance and access control. It is concerned with the problem of correctly identifying face images and assigning them to persons in a database. This paper proposes using SIFT features [4] for the recognition process. The new technique is compared with well-established face recognition algorithms, namely Eigenfaces [7] and Fish...

Hemifacial microsomia is a rare congenital malformation of craniofacial structures. Its characteristic features are unilateral underdevelopment of the face and ear malformations. This study describes clinical and radiographical features of a rare case of a 4-year-old hemifacial microsomia patient with underdevelopment of the left side of the face and preauricular skin tags on the affected side...

This paper is based on a combination of the principal component analysis (PCA), eigenface and support vector machines. Using N-fold method and with respect to the value of N, any person’s face images are divided into two sections. As a result, vectors of training features and test features are obtain ed. Classification precision and accuracy was examined with three different types of kernel and...

As a first step to the instance search task, we employed several existing image retrieval methods in combination (local feature matching, region matching, and global feature matching), according to the type and property of query images. The best run in this approach was ranked 23rd of 42 as regards the average precision result. Keywords; PCA-SURF; Bag of Features; Color histogram; Haar-Wavelet;...

Face detection has been an important research topic over the last 20 years. It is commonly used as a first step in face recognition and several techniques were applied in face detection, going from geometrical methods such as model matching to connectionist methods such as neural networks. This work presents a face detection system that uses a Bayesian network to combine information from differ...