University of Oulu

Learning and recognizing faces: from still images to video sequences

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Author: Hadid, Abdenour
Organizations: University of Oulu, Faculty of Technology, Department of Electrical and Information Engineering
University of Oulu, Infotech Oulu
Format: eBook
Online Access: PDF Full Text (PDF, 3.5 MB)
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Language: English
Published: 2005
Publish Date: 2005-06-13
Thesis type: Doctoral Dissertation
Defence Note: Academic Dissertation to be presented with the assent of the Faculty of Technology, University of Oulu, for public discussion in Auditorium TS 101, Linnanmaa, on June 23rd, 2005, at 12 noon
Reviewer: Doctor Joni-Kristian Kämäräinen
Professor Stan Z. Li


Automatic face recognition is a challenging problem which has received much attention during recent years due to its many applications in different fields such as law enforcement, security applications, human-machine interaction etc. Up to date there is no technique that provides a robust solution for all situations and different applications.

From still gray images to face sequences (and passing through color images), this thesis provides new algorithms to learn, detect and recognize faces. It also analyzes some emerging directions such as the integration of facial dynamics in the recognition process.

To recognize faces, the thesis proposes a new approach based on Local Binary Patterns (LBP) which consists of dividing the facial image into small regions from which LBP features are extracted and concatenated into a single feature histogram efficiently representing the face image. Then, face recognition is performed using a nearest neighbor classifier in the computed feature space with Chi-square as a dissimilarity metric. The extensive experiments clearly show the superiority of the proposed method over the state-of the-art algorithms on FERET tests.

To detect faces, another LBP-based representation which is suitable for low-resolution images, is derived. Using the new representation, a second-degree polynomial kernel SVM classifier is trained to detect frontal faces in complex gray scale images. Experimental results using several complex images show that the proposed approach performs favorably compared to the state-of-art methods. Additionally, experiments with detecting and recognizing low-resolution faces are carried out to demonstrate that the same facial representation can be efficiently used for both the detection and recognition of faces in low-resolution images.

To detect faces when the color cue is available, the thesis proposes an approach based on a robust model of skin color, called a skin locus, which is used to extract the skin-like regions. After orientation normalization and based on verifying a set of criteria (face symmetry, presence of some facial features, variance of pixel intensities and connected component arrangement), only facial regions are selected.

To learn and visualize faces in video sequences, the recently proposed algorithms for unsupervised learning and dimensionality reduction (LLE and ISOMAP), as well as well known ones (PCA, SOM etc.) are considered and investigated. Some extensions are proposed and a new approach for selecting face models from video sequences is developed. The approach is based on representing the face manifold in a low-dimensional space using the Locally Linear Embedding (LLE) algorithm and then performing K-means clustering.

To analyze the emerging direction in face recognition which consists of combining facial shape and dynamic personal characteristics for enhancing face recognition performance, the thesis considers two factors (face sequence length and image quality) and studies their effects on the performance of video-based systems which attempt to use a spatio-temporal representation instead of a still image based one. The extensive experimental results show that motion information enhances automatic recognition but not in a systematic way as in the human visual system.

Finally, some key findings of the thesis are considered and used for building a system for access control based on detecting and recognizing faces.

Series: Acta Universitatis Ouluensis. C, Technica
ISSN-E: 1796-2226
ISBN: 951-42-7759-7
ISBN Print: 951-42-7758-9
Issue: 223
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