{"id":{"repo_id":"bradford","oai_identifier":"oai:bradscholars.brad.ac.uk:10454/20734"},"canonical_url":"https://search.dev.ndltd.org/etd/bradford/oai:bradscholars.brad.ac.uk:10454/20734","repository":{"repo_id":"bradford","name":"University of Bradford","base_url":"https://bradscholars.brad.ac.uk/oai/request"},"display":{"title":"Addressing the Interclass Similarity Challenges in Automated Face Recognition","abstract":"Precisely recognising siblings through facial recognition poses a significant challenge, primarily due to the pronounced similarities found in sibling’s faces. This research assesses the effectiveness of computational face recognition models in distinguishing between siblings, conducting a comprehensive analysis of both global facial features and specific attributes. Leveraging datasets from SiblingDB, we have devised four customised frameworks aimed at discerning closely resembling sibling faces. This examination explores accuracy fluctuations across various facial regions. The investigation starts with assessing sibling differentiation accuracy utilising state-of-the-art pre-trained face recognition models, incorporating diverse facial features and established similarity measures to predict the accuracy. Second framework integrates an aggregate model, incorporating a custom object detector to identify facial parts. Informed by the first framework's analysis, this approach distinguishes between siblings using the optimal face recognition model and similarity measure. Next framework adopts a fusion strategy, enhancing result robustness by amalgamating face recognition models and classifiers from prior experiments. This fusion approach addresses discrepancies, ensuring a robust outcome by considering various model combinations and similarity measures to discriminate between siblings. The research concludes by addressing threshold dependency through a fine-tuned framework, incorporating the Softmax activation function as the final layer in advanced face recognition models. This final layer predicts whether input images depict the same or different individuals, mitigating challenges associated with threshold variations. Overall, this study contributes innovative approaches and insights, advancing the realism of discriminating siblings based on diverse facial features and unravelling the complexities of face recognition in differentiating individuals with high facial similarity.","abstract_html":"Precisely recognising siblings through facial recognition poses a significant challenge, primarily due to the pronounced similarities found in sibling’s faces. This research assesses the effectiveness of computational face recognition models in distinguishing between siblings, conducting a comprehensive analysis of both global facial features and specific attributes. Leveraging datasets from SiblingDB, we have devised four customised frameworks aimed at discerning closely resembling sibling faces. This examination explores accuracy fluctuations across various facial regions. The investigation starts with assessing sibling differentiation accuracy utilising state-of-the-art pre-trained face recognition models, incorporating diverse facial features and established similarity measures to predict the accuracy. Second framework integrates an aggregate model, incorporating a custom object detector to identify facial parts. Informed by the first framework's analysis, this approach distinguishes between siblings using the optimal face recognition model and similarity measure. Next framework adopts a fusion strategy, enhancing result robustness by amalgamating face recognition models and classifiers from prior experiments. This fusion approach addresses discrepancies, ensuring a robust outcome by considering various model combinations and similarity measures to discriminate between siblings. The research concludes by addressing threshold dependency through a fine-tuned framework, incorporating the Softmax activation function as the final layer in advanced face recognition models. This final layer predicts whether input images depict the same or different individuals, mitigating challenges associated with threshold variations. Overall, this study contributes innovative approaches and insights, advancing the realism of discriminating siblings based on diverse facial features and unravelling the complexities of face recognition in differentiating individuals with high facial similarity.","abstract_has_math":false,"creators":["Goel, Rita"],"institution":"University of Bradford","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Ugail, Hassan","Mehmood, Irfan"],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T01:13:57Z","subjects":["Face recognition","Interclass similarity analysis","Sibling identification","Deep learning","Pre-trained models","Facial similarity"],"languages":["en"],"rights":["\"Creative
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The investigation starts with assessing sibling differentiation accuracy utilising state-of-the-art pre-trained face recognition models, incorporating diverse facial features and established similarity measures to predict the accuracy. Second framework integrates an aggregate model, incorporating a custom object detector to identify facial parts. Informed by the first framework's analysis, this approach distinguishes between siblings using the optimal face recognition model and similarity measure. Next framework adopts a fusion strategy, enhancing result robustness by amalgamating face recognition models and classifiers from prior experiments. This fusion approach addresses discrepancies, ensuring a robust outcome by considering various model combinations and similarity measures to discriminate between siblings. The research concludes by addressing threshold dependency through a fine-tuned framework, incorporating the Softmax activation function as the final layer in advanced face recognition models. This final layer predicts whether input images depict the same or different individuals, mitigating challenges associated with threshold variations. Overall, this study contributes innovative approaches and insights, advancing the realism of discriminating siblings based on diverse facial features and unravelling the complexities of face recognition in differentiating individuals with high facial similarity."]},{"key":"dc:title","label":"Title","values":["Addressing the Interclass Similarity Challenges in Automated Face Recognition"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ugail, Hassan","Mehmood, Irfan"],"dc:creator":["Goel, Rita"],"dc:date.accessioned":["2026-01-13T14:58:38Z"],"dc:date.available":["2026-01-13T14:58:38Z"],"dc:description.abstract":["Precisely recognising siblings through facial recognition poses a significant challenge, primarily due to the pronounced similarities found in sibling’s faces. This research assesses the effectiveness of computational face recognition models in distinguishing between siblings, conducting a comprehensive analysis of both global facial features and specific attributes. 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The University of Bradford theses are licenced under a Creative Commons Licence."],"dc:subject":["Face recognition","Interclass similarity analysis","Sibling identification","Deep learning","Pre-trained models","Facial similarity"],"dc:title":["Addressing the Interclass Similarity Challenges in Automated Face Recognition"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["PhD"]},"updated_at":"2026-07-24T01:13:57Z"}