Mugshot Identification from Manipulated Facial Images
This addresses a challenge in forensic applications for law enforcement, but it is incremental as it applies existing methods to a new scenario.
The paper tackles the problem of identifying individuals from heavily manipulated facial images, proposing SIFT features for mugshot identification and comparing them with eigenface methods using real case images.
Editing on digital images is ubiquitous. Identification of deliberately modified facial images is a new challenge for face identification system. In this paper, we address the problem of identification of a face or person from heavily altered facial images. In this face identification problem, the input to the system is a manipulated or transformed face image and the system reports back the determined identity from a database of known individuals. Such a system can be useful in mugshot identification in which mugshot database contains two views (frontal and profile) of each criminal. We considered only frontal view from the available database for face identification and the query image is a manipulated face generated by face transformation software tool available online. We propose SIFT features for efficient face identification in this scenario. Further comparative analysis has been given with well known eigenface approach. Experiments have been conducted with real case images to evaluate the performance of both methods.