🤖 AI Summary
This work addresses the challenge of setting an appropriate distance threshold in Siamese verification networks by proposing an unsupervised method for automatic threshold determination. Relying on the assumption that the distribution of embedding distances exhibits a bimodal structure, the method dynamically identifies the local minimum between the two modes to establish the verification threshold. It requires no labeled data and supports real-time updates in deployment environments. Experimental results demonstrate that the proposed approach achieves an average verification accuracy of 94% across four benchmark datasets—MNIST, CIFAR-10, LFW, and PKLot—matching the performance of supervised equal error rate (EER)-based methods while substantially reducing the need for costly manual annotation.
📝 Abstract
Siamese verification networks are widely used to compare items such as faces, cars, or signatures. In these scenarios, the network is trained to learn an embedding space in which similar objects are mapped closer together, while dissimilar objects are mapped further apart. Two objects are considered to belong to the same class (e.g., the same person in two different images) when the distance between their embeddings falls below a predefined threshold. Defining this threshold, however, is a non-trivial task and typically requires labeled data. In this work, we assume that the distribution of distances produced by a siamese verification network can be approximated by a bimodal function. Based on this assumption, we propose an unsupervised method to determine the verification threshold by identifying the minimum point between the two modes. The proposed approach does not require annotated samples, enabling the verification threshold to be updated directly in the deployment environment without the cost of manual labeling. We evaluate our method on four datasets: MNIST, CIFAR-10, LFW, and PKLot. The results indicate that the proposed approach achieves an average verification accuracy of 94%, comparable to the Equal Error Rate method, while eliminating the need for labeled data.