Implementation of DeepFace for Gender Prediction Based on Facial Images - Wijaya, Aditya; Dwi Langit, Sadam; Musyaffa, Abdurrozzaq (Primakara University, 2026)
This study evaluates the performance of a pretrained DeepFace model for gender classification based on facial images using the UTKFace dataset. A total of 100 facial images were employed as test data, consisting of 50 male and 50 female samples selected through controlled random sampling to maintain class balance. Image preprocessing was conducted automatically using the DeepFace.analyze() function, which includes face detection, alignment, size normalization, and facial cropping. The study did not involve model retraining and relied solely on the inference capability of the pretrained DeepFace model. The experimental results show that the model correctly classified 45 male and 44 female images, achieving accuracies of 90% and 88% for the male and female classes, respectively, with an overall accuracy of 89%. Confusion matrix analysis indicates that misclassifications were primarily influenced by image quality factors such as lighting variations, camera angles, and facial expressions. Overall, the findings demonstrate that DeepFace is effective for gender classification without retraining; however, further improvements in preprocessing techniques and dataset diversity may enhance classification performance in future research.