Penerapan Deepface dan Retinaface dalam Pengenalan Wajah Parsial Untuk Aplikasi Keamanan Digital

Helmi Yulianti Fauziah; Tubagus Maulana Kusuma

doi:10.35760/ik.2025.v30i2.241

Penulis

Helmi Yulianti Fauziah Universitas Gunadarama Penulis
Tubagus Maulana Kusuma Universitas Gunadarama Penulis

DOI:

https://doi.org/10.35760/ik.2025.v30i2.241

Kata Kunci:

Pengenalan wajah parsial, RetinaFace, ArcFace, deep learning, biometrik

Abstrak

Pengenalan wajah merupakan aspek penting dalam teknologi keamanan modern dan memiliki banyak aplikasi. Namun demikian teknologi ini juga menghadapi tantangan, terutama dalam mengidentifikasi wajah yang hanya sebagian terlihat akibat berbagai kondisi seperti tertutup masker, kacamata atau benda lain. Penelitian ini mengusulkan solusi penegnalan wajah parsial berbasis deep learning dengan mengintegrasikan RetinaFace sebagai detektor wajah dan ArcFace yang diimplementasikan melalui framework DeepFace sebagai ekstraktor fitur untuk meningkatkan akurasi identifikasi wajah tidak utuh. Dataset dikumpulkan menggunakan kamera ponsel dengan variasi pencahayaan, ekspresi, dan oklusi (masker/kacamata), kemudian diproses melalui pipeline prapengolahan citra yang mencakup konversi warna, resizing, dan normalisasi. Model dilatih dengan loss function berbasis angular margin untuk memaksimalkan jarak antar kelas, dioptimasi menggunakan Adam (learning rate 0.001) dan dievaluasi melalui confusion matrix. Hasil pengujian menunjukkan akurasi 92% pada citra statis dan 94% dalam skenario realtime, dengan kesalahan prediksi terutama terjadi pada wajah tertutup lebih dari 50%. Keunggulan sistem ini terletak pada kombinasi deteksi multi-tugas RetinaFace (landmark + bounding box) dan ekstraksi fitur diskriminatif ArcFace, yang terbukti robust terhadap variasi parsial. Temuan ini mendukung aplikasi praktis seperti absensi digital dan keamanan berbasis kamera, dengan rekomendasi peningkatan kualitas dataset dan hardware untuk mengurangi false negative.

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