Face recognition from still images and video sequences
Başlık çevirisi mevcut değil.
- Tez No: 401553
- Danışmanlar: DOÇ. DR. HASAN DEMİREL
- Tez Türü: Doktora
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2009
- Dil: İngilizce
- Üniversite: Doğu Akdeniz Üniversitesi-Eastern Mediterranean University
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 155
Özet
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Özet (Çeviri)
In this thesis, two of the most well-known statistical approaches, namely principal component analysis (PCA) and linear discriminant analysis (LDA), have been used for feature extraction and dimensionality reduction for face recognition problem. Feedforward neural networks (FFNN) were utilized to improve the recognition performance by incorporating the discriminating power of the neural networks in the classification process. Multiresolution face recognition using discrete wavelet transform (DWT) was also investigated. Images at varying resolutions were generated by using DWT and then feature vectors were extracted from PCA and LDA spaces. Two data fusion methods have been proposed, where the first method utilized multiresolution feature concatenation (MFC) approach, which concatenated PCA and LDA projected feature vectors in different subbands for face recognition. The second method which is called the multiresolution majority voting (MMV), performed classification on each subband and fused the decisions coming from each subband, using majority voting to generate the overall decision. Finally, in the context of still image face recognition, we utilized complex approximately analytic wavelets, which possess Gabor like characteristics. We employed recently developed dual-tree complex wavelet transform (DT-CWT) and the single-tree wavelet transform (ST-CWT) for face recognition. Complex approximately analytic wavelets enjoy a much less redundant representation, which is computationally more efficient than Gabor wavelets and provide a local multiscale description of images with good directional selectivity and invariance to shifts and in plane rotations. Similar to Gabor wavelets they are insensitive to illumination variations and facial expression changes. The computational complexity analysis of both Gabor and complex wavelets were investigated. In this context, the superiority of the complex wavelets over the Gabor wavelets has been shown. These findings indicate that complex wavelet transform provides a strong alternative to Gabor wavelet transform for face recognition. Furthermore, the newly introduced ST-CWT having improved directional selectivity and shift invariance properties have shown better face recognition performance than DT-CWT. In addition to the face recognition in still images, two adaptive face recognition approaches for video sequences have been proposed. In the case of adaptive approach with updating gallery set, the gallery is updated at each frame by discarding outlier images from the set. Discarding of the gallery images depends on a proposed novel fitness measure. A fitness measure for each sample image in the gallery set is maintained and the fitness is updated after the processing of each frame of the probe video sequence. The images on the gallery with the lowest accumulated fitness values are discarded at each frame. At the end of the probe video, the person with the highest accumulated fitness value is declared to be the identified person. The other approach employed a fixed gallery and accumulated the fitness without discarding images from the set. Both approaches benefits from the proposed novel fitness measure to recognize subject in a given video sequence. Both approaches have been compared with conventional PCA and LBP methods. The results demonstrated the superiority of the proposed approach over the compared methods.
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