基于孿生網(wǎng)絡(luò)的小樣本人臉識別研究

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中圖分類號：TP391.41 文獻(xiàn)標(biāo)志碼：A 文章編號:1003-5168(2025)14-0023-05

DOI:10.19968/j.cnki.hnkj.1003-5168.2025.14.004

Research on Few-Shot Face Recognition Based on Siamese Networks

XU Qinan XIA ChunguanLIN Yuqing (ZhouKou Normal University, Zhoukou 466oo0, China)

Abstract: [Purpose] This study aims to explore few-shot face recognition technology based on Siamese networks to address the performance degradation of face recognition systems caused by data scarcity. [Methods]An enhanced Siamese network model is constructed and integrated with the MTCNN face detection algorithm to achieve precise facial region extraction.A mutual information metric is introduced to measure feature similarity,where the mutual information value between the joint distribution and marginal distribution of dual-branch feature vectors serves as the similarity criterion.By combining a contrastive loss function，the feature embedding space is optimized to maximize mutual information forintra-class samples while minimizing it for inter-class samples,thereby enhancing feature discriminabilityunder few-shot conditions.Additionaly,mutual information is normalized to the [O,1]interval to improve the robustness of similarity quantification，strengthening model stability and generalization.[Findings] Experimental results on the AT&T dataset demonstrate that the proposed method achieves a recognition accuracy of 98.64% under few shot conditions,outperforming traditional approaches such as PCA + SVM (63.87%), DeepFace (96. 11% ),and FaceNet ( 97.41% ),whichvalidates its effectiveness and innovation.[Conclusions] The enhanced Siamese network facilitates high-precision face recognition with limited data,providing novel insights and methodologies to the field of few-shot learning.

Keywords: siamese network; face recognition; few-shot learning; mutual information metric

0 引言

近年來，隨著人工智能技術(shù)的迅速發(fā)展，人臉識別已成為計算機(jī)視覺領(lǐng)域中一個備受關(guān)注的研究方向，在安防監(jiān)控、金融支付、社交媒體和智能家居等多種應(yīng)用中展現(xiàn)出巨大的潛力。（剩余8444字）