Design of a feature set for face recognition problem

An important problem in face recognition is the design of the feature space which represents the human face. Various feature sets have been and are continually being proposed for this purpose. However, there exists no feature set which gives a superior and consistent recognition performance on various face databases. Concatenating the popular features together and forming a high dimensional feature space introduces the curse of dimensionality problem. For this reason, dimensionality reduction techniques such as Principal Component Analysis is utilized on the feature space. In this study, first, some of the popular feature sets used in face recognition literature are evaluated over three popular face databases, namely ORL [1], UMIST [2], and Yale [3]. Then, high dimensional feature space obtained by concatenating all the features is reduced to a lower dimensional space by using the Minimal Redundancy Maximal Relevance [4] feature selection method in order to design a generic and successful feature set. The results indicate that mRMR selects a small number of features which are satisfactory and consistent in terms of recognition performance, provided that the face database is statistically stable with sufficient amount of data.


A comparison of features spaces for face recognition problem
OZYER, Gulsah Tumuklii; Akbaş, Emre; Yarman Vural, Fatoş Tunay (2006-04-19)
One of the most important problems in face recognition problem is designing the feature space which represents human face the "best". Concatenating the popular feature sets together and forming a high dimensional vector introduces the curse of dimensionality problem. For this reason, feature selection is required in order to reduce the dimension of the feature space. In this study, popular feature sets used in face recognition literature are considered and comparison between these sets is done. Furthermore,...
Unsupervised Metric Learning for Face Identification in TV Video
Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2011-01-01)
The goal of face identification is to decide whether two faces depict the same person or not. This paper addresses the identification problem for face-tracks that are automatically collected from uncontrolled TV video data. Face-track identification is an important component in systems that automatically label characters in TV series or movies based on subtitles and/or scripts: it enables effective transfer of the sparse text-based supervision to otherfaces. We show that, without manually labeling any examp...
3D face recognition with local shape descriptors
İnan, Tolga; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2011)
This thesis represents two approaches for three dimensional face recognition. In the first approach, a generic face model is fitted to human face. Local shape descriptors are located on the nodes of generic model mesh. Discriminative local shape descriptors on the nodes are selected and fed as input into the face recognition system. In the second approach, local shape descriptors which are uniformly distributed across the face are calculated. Among the calculated shape descriptors that are discriminative fo...
Infrared face recognition
Konuk, Uğur; Akar, Gözde; Department of Electrical and Electronics Engineering (2015)
Face recognition is a leading biometrics technique that fulfills the increasing need to identify a person in today’s world. Face recognition also has broad range of utilization, such as commercial and law enforcement applications. That is the reason why it still gathers a lot of attention and is an active research topic. Nevertheless visible spectrum face recognition algorithms are not free of challenges. Illumination, pose, expression variances and existence of facial disguises still degrade the performanc...
GAYE: A face recognition system
Kepenekci, B; Tek, FB; Cilingir, O; Sakarya, U; Akar, Gözde (2004-01-21)
In this paper, a new face recognition system, GAYE, is presented. GAYE is a fully automatic system that detects and recognizes faces in cluttered scenes. The input of the system is any digitized image/image sequence that includes face/faces. The basic building blocks of the system are face detection, feature extraction and feature comparison. Face detection is based on skin color segmentation. For feature extraction, a novel approach is proposed that depends on the Gabor wavelet transform of the face image....
Citation Formats
E. Akbaş, “Design of a feature set for face recognition problem,” 2006, vol. 4263, Accessed: 00, 2020. [Online]. Available: