Smart employee trackingface liveness and spoofing detection

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dc.contributor.authorMuhammad Hassan Javed
dc.contributor.authorHamza Manzoor
dc.date.accessioned2026-02-15T16:31:36Z
dc.date.available2026-02-15T16:31:36Z
dc.date.issued2022
dc.description.abstractBiometrics have emerged as a reliable solution for security systems, with facial recognition being one of the most heavily relied on and widely used biometric method for authorization. Face anti-spoofing technologies are critical in protecting face biometric systems from spoofing attacks and malicious digital manipulation. Although high accuracies have been achieved from various studies, The problem of existing approaches' generalization remain uncertain and there still remains a significant gap for improvement as new emerging spoofing attacks, difficult illumination conditions and varying environments can really take a toll on the models' performance coupled with less diverse and less abundant data available on this domain. Addressing this problem, we propose a study by expanding the horizon of the studies done on face liveness and spoof detection by working on other color spaces such as LAB and HSV in order to potentially find more generalization features for identification of spoofs, and whether further studies should explore this dimension. The study is performed with one of the largest datasets available yet on face liveness and spoofing detection with such diverse sets of illumination conditions, environments and subjects. We have performed experiments in classification of spoofing in different color spaces and concluded that LAB and HSV is not up to the par in classification of spoofs. The other part of the study composed of training the model on combined dataset of LAB and HSV color space. The results from that model also could not beat the model trained on RGB color space. The RGB was the best performing model and the API was deployed using that model.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/19778
dc.language.isoen
dc.publisherUMT Lahore
dc.titleSmart employee trackingface liveness and spoofing detection
dc.typeThesis
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