Face classification with support vector machine

Date

2004-04-30

Author

Kepenekci, B
Akar, Gözde

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

169
views

0
downloads

A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the last decision is made by considering all of those labels. By using Gabor features the number of support vectors is reduced compared to directly using the actual image data, and. also a better generalization performance is achieved.

Subject Keywords

recognition

URI

https://hdl.handle.net/11511/36709

DOI

https://doi.org/10.1109/siu.2004.1338596

Conference Name

IEEE 12th Signal Processing and Communications Applications Conference

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

3D Data Processing for Enhancement of Face Scanner Data BOZKURT, Nesli; Halıcı, Uğur; ULUSOY PARNAS, İLKAY; AKAGUNDUZ, Erdem (2009-11-11) The data acquired by 3D face scanners have distortions such as spikes, holes and noise. Enhancement of 3D face data by removing these distortions while keeping the face features is important for the applications using these data. In this study, thresholding is used for removing spikes, thresholding together with face symmetry, is used for hole filling and bilateral filtering is used for smoothing and satisfactory results are obtained on FRGC 3D face data.
A comparison on textured motion classification Oztekin, Kaan; Akar, Gözde (2006-01-01) Textured motion - generally known as dynamic or temporal texture analysis, classification, synthesis, segmentation and recognition is popular research areas in several fields such as computer vision, robotics, animation, multimedia databases etc. In the literature, several algorithms are proposed to characterize these textured motions such as stochastic and deterministic algorithms. However, there is no study which compares the performances of these algorithms. In this paper, we carry out a complete compari...
CoSPAIR: Colored Histograms of Spatial Concentric Surflet-Pairs for 3D object recognition Logoglu, K. Berker; Kalkan, Sinan; Temizel, Alptekin (2016-01-01) Introduction of RGB-D sensors together with the efforts on open-source point-cloud processing tools boosted research in both computer vision and robotics. One of the key areas which have drawn particular attention is object recognition since it is one of the crucial steps for various applications. In this paper, two spatially enhanced local 3D descriptors are proposed for object recognition tasks: Histograms of Spatial Concentric Surflet-Pairs (SPAIR) and Colored SPAIR (CoSPAIR). The proposed descriptors ar...
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...
Fingerprint classification through self-organizing feature maps modified to treat uncertainties Halıcı, Uğur (1996-10-01) In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. For this purpose, the concept of ''certainty'' is introduced and used in the modified algorithms. This fingerprint classifier together with a fingerprint identifier, constitute subsystems of an automated fingerprint identification system named...

Citation Formats

B. Kepenekci and G. Akar, “Face classification with support vector machine,” presented at the IEEE 12th Signal Processing and Communications Applications Conference, Kusadasi, TURKEY, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36709.