Estimation of sex through morphometric landmark indices in facial images with strength of evidence in logistic regression analysis
Rajesh Verma, Navdha Bhardwaj, Pushap Deep Singh, Arnav V. Bhavsar, Vishal Sharma · Forensic Science International Reports · 2021
We report an investigation into the application of the logistic regression classifier for estimation of sex in facial images. We used 2000 images, 1000 each of both sexes from a publicly available database and automatically detected facial landmarks and derived some morphometric facial indices. These indices were used as predictors for the classification. As the traditional manual extraction of facial landmarks is time consuming, automatic detection of the landmarks improves the efficiency. The logistic regression classification is also compared with two other classification methods, the likelihood-ratio (LR) based method where the features of a face are evaluated in terms of the probability distribution of these features in both the sexes, and the Convolutional Neural Networks (CNN) methods. While is former is desirable from the viewpoint of interpretability and to assess the strength of evidence, the latter is sophisticated. We report an AUC of 0.94 with true positive (TP) rate of 88.4% for males and 87.9% for females for logistic regression-based classification. This performance is better than the likelihood ratio classifier with TP rate of 79.6% for males and 82.2% for females. The overall performance of logistics regression is slightly less than the CNN classifier that has 89.3% TP rate for males and 92.6% for females. We have extended these models to a CCTV image database, more representative of the forensic scenario and found the logistic regression performing better than the CNN method on an average for 8 different types of cameras. We conclude that as a trade-off between simplicity and sophistication, the logistic regression classifier can be used for a two-class problem like classification of sex from facial morphometric indices, and that the likelihood ratio approach can assess the strength of the classification, in conformance with the requirements of evidence interpretation.