Visual Analysis of Facial Features using a Convolutional Neural Network (CNN) to Provide Styling Recommendations

Irfan Rizki Fadhilah (Universitas Bina Sarana Informatika)
Rafli Adi Septiyan (Universitas Bina Sarana Informatika)
Syifa Nur Rakhmah (Universitas Bina Sarana Informatika)
Findi Ayu Sariasih (Universitas Bina Sarana Informatika)
Imam Sutoyo (Universitas Bina Sarana Informatika)

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 6 No. 1 (2026): October 2026

This study aims to develop a facial shape analysis system that provides hairstyle recommendations based on face type using the Convolutional Neural Network (CNN) method. The dataset consists of face images categorized into four labels: heart, oblong, round, and square. The preprocessing process includes image resizing and normalization to improve model accuracy. The MobileNetV2 architecture is employed for feature extraction and classification due to its efficiency in handling image-based recognition tasks. The model achieved an accuracy of 77%, indicating that it can effectively classify face shapes and generate hairstyle recommendations. The system is deployed as a web application, allowing users to upload their photos, automatically identify face shapes, and receive hairstyle suggestions that best match their facial characteristics. This research demonstrates the potential of CNN in implementing intelligent styling recommendations based on visual facial features.