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

Irfan Rizki Fadhilah; Rafli Adi Septiyan; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo

doi:10.59934/jaiea.v6i1.2670

Authors

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

DOI:

https://doi.org/10.59934/jaiea.v6i1.2670

Keywords:

Face Shape; Convolutional Neural Network; MobileNetV2; Image Classification; Hairstyle Recommendation

Abstract

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.

Downloads

Download data is not yet available.

References

M. Pandia, P. Sihombing, P. Simamora, and R. Kaban, “Kajian Literatur Multimedia Retrieval : Machine Learning Untuk Pengenalan Wajah,” vol. 7, pp. 161–166, 2024.

M. Fauzi et al., “Implementasi Machine Learning Untuk Memprediksi Cuaca Menggunakan Support Vector Machine Pendahuluan Metode Penelitian,” vol. 23, pp. 45–50, 2024.

A. D. Ulhaq and I. K. D. Nuryana, “Sistem Rekomendasi Gaya Rambut Personal Berdasarkan Analisis Wajah dan Rambut,” vol. 07, pp. 340–347, 2025.

E. Q. B. Tafifasari, “Efek Perbedaan Hasil Styling Terhadap Jenis Rambut,” e-Journal, vol. 09, no. 2, pp. 166–172, 2020.

M. S. Azzahra, S. S. Maesaroh, and R. G. Guntara, “Penggunaan Convolutional Neural Network dan Transfer Learning untuk Rekomendasi Gaya Rambut Pria,” J. Algoritm., vol. 21, no. 2, pp. 173–183, 2024, doi: 10.33364/algoritma/v.21-2.2134.

M. Y. Putra, “Rancang Bangun Deteksi Bentuk Wajah Untuk Menentukan Gaya Rambut Menggunakan Algoritma CNN,” Repeater Publ. Tek. Inform. dan Jar., vol. 2, no. 3, pp. 206–212, 2024, doi: 10.62951/repeater.v2i3.139.

N. Chalista et al., “Identifikasi Pengenalan Wajah Berdasarkan Jenis Kelamin Menggunakan Metode Convolutional Neural Network ( CNN ),” vol. 6, no. 1, 2024, doi: 10.37802/joti.v6i1.694.

Y. B. Widodo, S. Sibuea, and A. Rivaldi, “Rancang Bangun Aplikasi Sistem Pakar untuk Pemilihan Model Gaya Rambut Pria Menggunakan Metode Forward Chaining,” J. Teknol. Inform. dan Komput., vol. 9, no. 1, pp. 558–573, 2023, doi: 10.37012/jtik.v9i1.1622.

D. Lutfiati and D. S. Megasari, “PENERAPAN MODEL PEMBELAJARAN BERDASARKAN MASALAH PADA KOMPETENSI DASAR PELURUSAN RAMBUT YANG TELAH MENGALAMI S1 Pendidikan Tata Rias , Fakultas Teknik , Universitas Negeri Surabaya S1 Pendidikan Tata Rias , Fakultas Teknik , Universitas Negeri Surabaya,” vol. 09, 2020.

A. Maghfiroh et al., “PENGARUH MEDIA SOSIAL TERHADAP PEMILIHAN,” vol. 2, no. 3, pp. 11–20, 2025.