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This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation. Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images. Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model

This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation. Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images. Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model

This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation. Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images. Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model

This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation. Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images. Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model

This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation. Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images. Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model

This paper is addressing a challenging face recognition problem: Face Identification From One Single Face Image. We present a novel approach to face identification, which is capable to identify a person from face images that are significantly different from the sample image in terms of illumination, camera view angles and expressions, The approach is based on a new measurement of dissimilarily between the two face images. A person is identified based on the smallest dissimilarity, which is the Summation of the dissimilarilies of all pairs of observations extracted from the face image on both vertical and horizontal directions. Our experiment results tested on both the AR face database and the CMU PIE face database show that the proposed method outperforms the PCA, LDA, LFA based approaches.

This paper presents a novel approach for recognizing faces in images taken from different illumination, expression, near frontal pose, partially occlusion and time delay. The method is based on one dimensional discrete Hidden Markov Model (1D-DHMM) with new way of extracting observations and using observation sequences. All subjects in the system share only one HMM that is used as a means to weigh a pair of observations. The Haar wavelet transform is applied to face images to reduce the dimension of the observation vectors. The selection of the recognized person is based on the highest score, which is the summation of the likelihoods of all observation sequences extracted from image on both vertical and horizontal dimensions. Our experiment results tested on the AR face database and the CMU PIE face database show that the proposed method outperforms the PCA, LDA, LFA based approaches tested on the same databases.

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth