The investigation of human face images is ubiquitous in pattern analysis/ image processing research. Traditional approaches are related to face identification and verification but, several other areas are emerging, like age/ expression estimation, analysis of facial similarity and attractiveness and automatic kinship recognition. Despite the fact that the latter could have applications in fields such as image retrieval and annotation, little work in this area has been presented so far. This thesis presents an algorithm able to discriminate between siblings and unrelated individuals, based on their face images. In this context, a great challenge was to deal with the lack of a benchmark in kinship analysis, and for this reason, a high-quality dataset of images of siblings’ pairs was collected. This is a relevant contribution to the research community and is particularly useful to avoid potential problems due to low quality pictures and uncontrolled imaging conditions of heterogeneous datasets used in previous researches. The database includes frontal, profile, expressionless and smiling faces of siblings pairs. Based on these images, various classifiers were constructed using feature-based and holistic techniques to investigate which data are more effective for discriminating siblings from non-siblings. The features were first tested individually and then the most significant face data were supplied to a unique algorithm. The siblings classifier has been found to outperform human raters on all datasets. Also, the good discrimination capabilities of the algorithm is tested by applying the classifiers to a low quality database of images collected from the Internet in a cross-database experiment. The knowledge acquired from the analysis of siblings fostered a similar algorithm able to discriminating parent-child pairs from unrelated individuals. The results obtained in this thesis have impact in image retrieval and annotation, forensics, genealogical research and finding missing family members.
Identifying Kinship Cues from Facial Images / FIGUEIREDO VIEIRA, Tiago. - (2013). [10.6092/polito/porto/2521491]
Identifying Kinship Cues from Facial Images
FIGUEIREDO VIEIRA, TIAGO
2013
Abstract
The investigation of human face images is ubiquitous in pattern analysis/ image processing research. Traditional approaches are related to face identification and verification but, several other areas are emerging, like age/ expression estimation, analysis of facial similarity and attractiveness and automatic kinship recognition. Despite the fact that the latter could have applications in fields such as image retrieval and annotation, little work in this area has been presented so far. This thesis presents an algorithm able to discriminate between siblings and unrelated individuals, based on their face images. In this context, a great challenge was to deal with the lack of a benchmark in kinship analysis, and for this reason, a high-quality dataset of images of siblings’ pairs was collected. This is a relevant contribution to the research community and is particularly useful to avoid potential problems due to low quality pictures and uncontrolled imaging conditions of heterogeneous datasets used in previous researches. The database includes frontal, profile, expressionless and smiling faces of siblings pairs. Based on these images, various classifiers were constructed using feature-based and holistic techniques to investigate which data are more effective for discriminating siblings from non-siblings. The features were first tested individually and then the most significant face data were supplied to a unique algorithm. The siblings classifier has been found to outperform human raters on all datasets. Also, the good discrimination capabilities of the algorithm is tested by applying the classifiers to a low quality database of images collected from the Internet in a cross-database experiment. The knowledge acquired from the analysis of siblings fostered a similar algorithm able to discriminating parent-child pairs from unrelated individuals. The results obtained in this thesis have impact in image retrieval and annotation, forensics, genealogical research and finding missing family members.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2521491
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