Open AccessInfluence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification
Author(s) -
Igor Vuković,
Petar Čisar,
Kristijan Kuk,
Miloš Bandjur,
B.D. Popović
Publication year - 2021
Publication title -
elektronika ir elektrotechnika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.224
H-Index - 26
eISSN - 2029-5731
pISSN - 1392-1215
DOI - 10.5755/j02.eie.29081
Subject(s) - sharpening , normalization (sociology) , computer science , histogram , artificial intelligence , extractor , identification (biology) , face (sociological concept) , pixel , computer vision , pattern recognition (psychology) , filter (signal processing) , feature (linguistics) , image (mathematics) , engineering , social science , linguistics , philosophy , botany , sociology , process engineering , anthropology , biology
In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.