Mugshot Identification from Manipulated Facial Images

TL;DR

This paper tackles the challenge of identifying faces from heavily manipulated images by proposing a SIFT-based method and comparing it with eigenface, demonstrating effectiveness in mugshot identification scenarios.

Contribution

It introduces a SIFT feature-based approach for identifying manipulated faces and compares its performance with eigenface methods in mugshot databases.

Findings

SIFT features outperform eigenface in manipulated face identification

The proposed method effectively identifies faces from altered images

Experimental results validate the approach on real case images

Abstract

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.