Latent fingerprint enhancement for accurate minutiae detection

Abdul Wahab, Tariq Mahmood Khan, Shahzaib Iqbal, Bandar AlShammari, Bandar Alhaqbani, Imran Razzak
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Abstract

Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition. Although fixed-length embeddings have shown effectiveness in recognising rolled and slap fingerprints, the methods for matching latent fingerprints have primarily centred around local minutiae-based embeddings, failing to fully exploit global representations for matching purposes. Consequently, enhancing latent fingerprints becomes critical to ensuring robust identification for forensic investigations. Current approaches often prioritise restoring ridge patterns, overlooking the fine-macroeconomic details crucial for accurate fingerprint recognition. To address this, we propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE) through a structured approach to fingerprint generation. By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances. This leads to a significant improvement in identification performance. Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features. Extensive evaluations conducted on two publicly available datasets demonstrate our method's dominance over existing state-of-the-art techniques, highlighting its potential to significantly enhance latent fingerprint recognition accuracy in forensic applications.
增强潜伏指纹,实现精确的细节检测
根据部分指纹和污损指纹(通常称为指印或潜指纹)识别嫌疑人是指纹识别领域的一项重大挑战。虽然固定长度的嵌入式指纹识别技术在识别卷曲和拍打指纹方面显示出了有效性,但潜伏指纹的匹配方法主要集中在基于细节的局部嵌入式指纹识别技术上,未能充分利用全局代表性来达到匹配目的。因此,增强潜伏指纹对于确保法医调查中的可靠识别至关重要。目前的方法通常优先考虑恢复脊纹模式,而忽略了对准确识别指纹至关重要的微观经济细节。为了解决这个问题,我们提出了一种新方法,即使用生成式反向网络(GAN),通过结构化的指纹生成方法重新定义潜伏指纹增强(LFE)。通过在生成过程中直接优化细节信息,该模型生成的增强潜伏指纹与地面实况的保真度极高。这大大提高了识别性能。我们的框架整合了细部特征位置和方位场,确保保留局部和结构性指纹特征。在两个公开可用的数据集上进行的广泛评估表明,我们的方法优于现有的最先进技术,突出了其在法医应用中显著提高潜伏指纹识别准确性的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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