A New Approach for Standardized Zero-Shot Face Verification Using Siamese Neural Networks with Dynamic Thresholding

Abstract

Face recognition and verification systems play a crucial role in many critical areas such as biometric security, access control, and user authentication. This study presents a training-free (zero-shot) face verification protocol and comprehensively compares the performance of different pre-trained deep learning models-Facenet-IRv1, ArcFace, ResNet-18, VGG16, AlexNet, and OpenFace-on the Labeled Faces in the Wild (LFW) dataset. In the proposed approach, two input images are passed through the same network using a Siamese-like inference process, and the resulting embeddings are compared using cosine similarity after L2-normalization. To classify the similarity scores obtained from the model outputs, dynamic threshold calibration is applied for each model, maximizing Youden's J statistic, and this threshold value (𝜏) is transferred to the test dataset without any additional optimization. Additionally, multiple metrics such as ROC-AUC curve, accuracy, precision, recall, F1-score, average inference time, and FPS were calculated to evaluate model performance independently of the threshold. The findings indicate that ArcFace and Facenet-IRv1 models surpassed others in terms of accuracy and reliability, while lightweight architectures such as ResNet-18 and VGG16 offer speed advantages, making them suitable alternatives for real-time applications. These results demonstrate that approaches that do not require training from scratch offer both a cost- and time-efficient solution in face verification systems. In this respect, the study introduces a standardized framework that enables a multidimensional evaluation of different architectures without the need for additional training and offers quantitative insights into the accuracy–speed trade-off in the field of face verification.

Keywords

• Zero-shot face verification
• Dynamic thresholding
• Face recognition

Dinamik Eşikleme ile Siamese Sinir Ağlarını Kullanarak Standartlaştırılmış Sıfır Atışlı Yüz Doğrulama için Yeni Bir Yaklaşım

Abstract

Yüz tanıma ve doğrulama sistemleri, biyometrik güvenlik, erişim kontrolü ve kullanıcı kimlik doğrulaması gibi birçok kritik alanda çok önemli bir rol oynamaktadır. Bu çalışma, eğitim gerektirmeyen (sıfır atışlı) bir yüz doğrulama protokolü sunmakta ve Labeled Faces in the Wild (LFW) veri kümesi üzerinde farklı önceden eğitilmiş derin öğrenme modellerinin (Facenet-IRv1, ArcFace, ResNet-18, VGG16, AlexNet ve OpenFace) performansını kapsamlı bir şekilde karşılaştırmaktadır. Önerilen yaklaşımda, iki giriş görüntüsü aynı ağdan Siamese benzeri bir çıkarım süreci kullanılarak geçirilir ve elde edilen gömülü veriler L2 normalleştirmesinden sonra kosinüs benzerliği kullanılarak karşılaştırılır. Model çıktılarından elde edilen benzerlik puanlarını sınıflandırmak için, her model için dinamik eşik kalibrasyonu uygulanır, Youden'in J istatistiği maksimize edilir ve bu eşik değeri (τ) ek optimizasyon yapılmadan test veri setine aktarılır. Ek olarak, eşik değerinden bağımsız olarak model performansını değerlendirmek için ROC-AUC eğrisi, doğruluk, kesinlik, duyarlılık, F1 skoru, ortalama çıkarım süresi ve FPS gibi çoklu metrikler hesaplandı. Bulgular, ArcFace ve Facenet-IRv1 modellerinin doğruluk ve güvenilirlik açısından diğerlerini geride bıraktığını, daha hafif yapılar olan ResNet-18 ve VGG16'nın ise hız avantajları sunduğunu ve bu sayede gerçek zamanlı uygulamalar için uygun alternatifler olduğunu göstermektedir. Bu sonuçlar, sıfırdan eğitim gerektirmeyen yaklaşımların yüz doğrulama sistemlerinde hem maliyet hem de zaman açısından verimli bir çözüm sunduğunu göstermektedir. Bu bağlamda, çalışma ek eğitim gerektirmeden farklı mimarilerin çok boyutlu değerlendirilmesini sağlayan standartlaştırılmış bir çerçeve sunmakta ve yüz doğrulama alanında doğruluk-hız takası hakkında nicel bilgiler sağlamaktadır.

Keywords

• Sıfır atışlı yüz doğrulama
• Dinamik eşikleme
• Yüz tanıma

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