自动潜在指纹识别系统(AFIS)在执法和刑事调查中被法医专家最广泛地使用。自动潜在指纹匹配中使用的关键步骤之一是从指纹图像中自动提取可靠的细节。因此,细节提取被认为是AFIS中非常重要的步骤。这种系统的性能在很大程度上依赖于输入指纹图像的质量。由于较差的脊线图案和背景噪声的存在,大多数最先进的AFIS未能产生良好的匹配结果。为了确保指纹匹配对低质量潜在指纹图像的鲁棒性,在细节提取和匹配之前,必须包含一个好的指纹增强算法。在本文中,我们提出了一个端到端的指纹匹配系统来自动增强,提取细节,并产生匹配的结果。为了实现这一点,我们提出了一种使用“自动深度卷积神经网络(DCNN)”和“快速傅里叶变换(FFT)”滤波器自动增强劣质指纹图像的方法。深度卷积神经网络(DCNN)根据指纹域知识生成频率增强图。我们提出了一种“FFT增强”算法来从频率增强图中增强和提取脊。使用拟议的“自动潜在细节提取器(ALME)”自动提取增强脊的细节。根据提取的细节,指纹自动对齐,并且使用提出的“频率增强细节匹配器(FEMM)”算法计算匹配分数。在FVC2002,FVC2004和NISTSD27潜在指纹数据库上进行了实验。细节提取结果显示精密度显著提高,召回,和F1得分。我们在FVC2002/2004中获得了最高的Rank-1识别率,为100%,在NISTSD27指纹数据库中获得了84.5%。匹配结果表明,所提出的系统优于最先进的系统。
Automatic Latent Fingerprint Identification Systems (
AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in
AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art
AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the \"Automated Deep Convolutional Neural Network (DCNN)\" and \"Fast Fourier Transform (FFT)\" filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an \"FFT Enhancement\" algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed \"Automated Latent Minutiae Extractor (ALME)\". Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed \"Frequency Enhanced Minutiae Matcher (FEMM)\" algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.