Identification of Facial Features in Face Templates Using Deep Neural Network Models
DOI:
https://doi.org/10.15837/ijccc.2026.2.7294Keywords:
face templates, face recognition, deep learning, machine learning, hyperparameter optimizationAbstract
In this research, facial features are identified from face templates using deep neural network models. Facial templates are widely used in modern biometric systems, enabling efficient and reliable individual recognition. Such templates are compact, easy to process, and are often used in real-time systems. During the research, a dataset with 115,023 items (face templates), used by the Neurotechnology company to identify persons, was prepared as a new dataset. The dataset has been prepared to study three facial features from face templates: gender, race, and age. The facial templates are lighter-weight than real photos and could be reused to estimate the facial features. However, it is not yet known how effective facial feature identification will be, as some data are lost when converting a photo into a face template. Several methods have been proposed for classifying original image attributes solely from face templates. Both deep neural networks and classical machine learning algorithms were used in the experiments. The experiments revealed that gender is the most accurately predictable attribute, with the best model achieving 93 % accuracy. Classification of people’s race and age from face templates is more challenging, likely because face template generation models are designed to eliminate information unrelated to identity. A comparison of machine learning methods showed that deep neural networks are better suited to this task than classical classification algorithms.
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