Suspect Identification using Local Facial Attributed by Fusing Facial Landmarks on the Forensic Sketch
Mohd Aamir Khan, Anand Singh Jalal · 2020
Faces are the most common biometric used for the identification of a person. Every person has different facial visual attributes that discriminate them from others. Law enforcement agencies use the face as a key evidence to identify the suspect, involved in unlawful activities. To identify the suspect sketches are used to apprehend the suspect. The sketch is the painting of the visual description given by the onlooker. Hand drawn sketch drawn by the sketch artist is uncertain. It depends on the description observation and memory of the eyewitness. Uncertainty of the face visual attributes is generally ignored by the existing methods. Face shape and texture are different in the sketch then the visual features of the facial photo. In this paper, we have proposed a method to retrieve the facial photos of the potential suspect using the sketch as a query. We have used the local facial visual attributes to identify facial features. Firstly, face key points are identified using 81 facial landmarks detector. After that, local facial attributes are extracted and features are calculated. We have utilized the Bayesian classification to retrieve the mugshot images. We have experimented the proposed method on the forensic dataset and compared with state-of-the-art methods. It is evident from the experimental result that the proposed model performs better with the state-of-the-art method methods on forensic sketch datasets.