Fingerprint Image Quality Research Articles

Automatic assessment of fingermarks quality: Exploration of the possible application in the context of detection and comparison with human examiners

In forensic science, particularly in the context of latent fingermarks detection, forensic scientists are often faced with the need to assess the quality of the detected fingermarks to quantitatively interpret their results and express conclusions. Today this process is mainly carried out by human examiners referring to guidelines or provided quality scales. The largest the set of fingermarks (e.g., hundreds, thousands), the longest and the most labor-intensive this task becomes. Moreover, it is difficult to guarantee a fully objective process since the subjectivity of each individual is almost impossible to avoid, especially with regards to the interpretation of the quality scale levels or when facing fingermarks detected in an inhomogeneous manner. In this paper, the possibility of automatizing the quality assessment step is explored. The choice has been made to consider the use of quality assessment algorithms currently applied in an identification context. 150 natural fingermarks from ten donors were deposited on three different supports. These marks were detected using 1,2-indanedione/zinc or cyanoacrylate fumigation depending on the support. Then, their quality was assessed by five examiners, according to the UNIL scale, and by seven algorithms (i.e., Lights Out, Latent Fingerprint Image Quality 1 and 2, Latent Quality Metric, Expected Score Likelihood Ratio, NIST Fingerprint Image Quality, MINDTCT). Spearman and Pearson correlations were calculated, and the distribution of scores for each algorithm was charted (using boxplots) against the results provided by the human examiners. The most promising results were obtained with the LQM algorithm, more specifically with the fingermark clarity metric.

Quality Assessment and Identification of Fingerprints by Analysis of the Image’s Structural Properties

The paper addresses the problem of assessing the quality of fingerprint images using spatial analysis methods. The author proposes using the previously developed mathematical model to describe the set of magnitudes of the image gradient. The model is based on the two-parameter Weibull distribution. The author proposes two approaches to assess the quality of fingerprints. The first approach is implemented by the so-called “Full reference method”, which compares the Weibull distribution parameters’ values of statistical estimates. The results of identifying sweat pores using this method are presented. The second approach is called the “No-Reference method” and is used to assess fingerprints’ quality when analyzing and identifying the information content of their individual sections. It is proposed to use an image blur map as a quality characteristic and a statistical estimate of the Weibull distribution shape parameter as a measure of the blur. The shape parameter is estimated at each image point by the combination of magnitudes of the image gradient in the vicinity of the point; in this, the previously developed blur mapping technique is applied. The specific examples illustrate effectiveness of the proposed approaches.

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement.

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.

Flexible fingerprint sensor driver IC and bendable evaluation

SummaryThe paper presents a flexible polyimide fingerprint sensor driver IC driven by a 0.18‐μm CMOS technology and studies the sensor bendable performance. The finger valley and ridge are sensitive to environment noise; therefore, we propose low‐ripple cross non‐overlap charge pump that performs 7.2 V pumped output voltage with Δ30 mV ripple and 98.36% pump efficiency to increase the sensing level and propose mutual Vref SAR ADC circuit with 6.6 V input swing range, 30 MS/s, ENOB of 10 bits, SNDR 74.1 dB, and bit error with INL [0.61, −0.7] and DNL [0.9, −0.62] to enhance the resolution. Through low‐ripple charge pump and mutual Vref SAR ADC circuitry implementation, it makes the sensing of fingerprint valley and ridge capacitance signal still distinguishable when the film is bent harshly by the substrate radius 10 mm and, in the meanwhile, meet the FBI PIV biometric fingerprint image quality, which is upmost tough standard in biometric fingerprint of identification product.

Fingerprint Quality Assessment based on Texture and Geometric Features

Abstract Fingerprint quality assessments are generally used to evaluate the quality of images obtained from fingerprint sensors, and effective fingerprint quality assessment methods are crucial to establishing high-performance biometric identification systems. The use of fingerprint quality assessments helps improve the accuracy of fingerprint registration and user satisfaction. NIST Fingerprint Image Quality (NFIQ) is a popular fingerprint quality assessment algorithm; however, it is unable to provide high-quality assessments for some partial fingerprint images obtained from mobile device sensors. In this study, a hybrid fingerprint assessment framework that integrated texture and geometric features was examined. The final quality assessment values obtained by the framework were higher than those obtained using NFIQ, effectively elevating the performance of existing NFIQ algorithms and expanding its scope of application for different fingerprint images.

PENINGKATAN KUALITAS CITRA SIDIK JARI DENGAN ALGORITMA MINUTIAE EXTRACTION DAN LEARNING VECTOR QUANTIZATION

Sistem pengenalan sidik jari bertujuan untuk mengindentifikasi seseorang. Kendala utama dalam pengenalan sidik jari seseorang, adalah karena citranya memiliki kualitas yang rendah. Kualitas tersebut disebabkan oleh jenis kulit yang berminyak, kering, kotor dan jenis scanner fingerprint yang digunakan. Untuk itu dilakukan peningkatan dengan tujuan memperbaiki citra sidik jari sebagai faktor utama penentu tingkat akurasi hasil pengenalan citra sidik jari. Agar citra sidik jari yang tidak jelas mudah diinterpretasi oleh manusia maupun mesin, maka perlu peningkatan kualitas citra tersebut dengan cara memperjelas garis sidik jarinya. Penelitian ini bertujuan untuk peningkatan kualitas citra sidik jari dengan Algoritma Minutiae Extraction dan metode Learning Vector Quantization (LVQ) sebagai pengujian. Pengujian ini dilakukan dengan menggunakan 4 citra sebagai pelatihan pada setiap kelasnya. Adapun jumlah kelas pada pengujian ini seluruhnya 10 kelas dengan tiap kelas terdiri dari 10 citra. Pada pengujian 75 data citra sidik jari diperoleh tingkat akurasi 83.3333%.

Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Distortions such as dryness, wetness, blurriness, physical damages and presence of dots in fingerprints are a detriment to a good analysis of them. Even though fingerprint image enhancement is possible through physical solutions such as removing excess grace on the fingerprint or recapturing the fingerprint after some time, these solutions are usually not user-friendly and time consuming. In some cases, the enhancements may not be possible if the cause of the distortion is permanent. In this paper, we are proposing an unpaired image-to-image translation using cycle-consistent adversarial networks for translating images from distorted domain to undistorted domain, namely, dry to not-dry, wet to not-wet, dotted to not-dotted, damaged to not-damaged, blurred to not-blurred. We use a database of low quality fingerprint images containing 11541 samples with dryness, wetness, blurriness, damages and dotted distortions. The database has been prepared by real data from VISA application centres and have been provided for this research by GEYCE Biometrics. For the evaluation of the proposed enhancement technique, we use VGG16 based convolutional neural network to assess the percentage of enhanced fingerprint images which are labelled correctly as undistorted. The proposed quality enhancement technique has achieved the maximum quality improvement for wetness fingerprints in which 94% of the enhanced wet fingerprints were detected as undistorted.

Fingerprint image quality assessment based on BP neural network with hierarchical clustering

Fingerprint image quality assessment is important because the good performance of the minutiae-based matching algorithm is heavily dependent on fingerprint images with high quality. Many efforts have been made in existing methods, but most methods either use full fingerprint images or use local areas and involve subjective judgments. Unlike previous methods, the proposed method considers both local and global assessments. Local feature vectors are extracted from the fingerprint image block for hierarchical clustering, and the results are used as target outputs of the back-propagation (BP) neural network without any subjective judgments. Global feature vectors based on the local quality assessment results are used for hierarchical clustering and fed into the BP neural network that calculates the overall error rate of genuine and imposter errors to achieve global quality assessment. Furthermore, the minutiae quality assessment method is also proposed and incorporated into the minutiae-based matching algorithm. The experimental results based on the FVC2002 and FVC2004 databases show that the proposed methods can effectively assess the quality of fingerprint images and ensure the overall improvement of matching performance.

Multi-task CNN for restoring corrupted fingerprint images

Fingerprint image enhancement is one of the fundamental modules in an automated fingerprint recognition system (AFRS). While the performance of AFRS advances with sophisticated fingerprint matching algorithms, poor fingerprint image quality remains a major issue to achieve accurate fingerprint recognition. In this paper, we present a multi-task convolutional neural network (CNN) based method to recover fingerprint ridge structures from corrupted fingerprint images. By learning from the noises and corruptions caused by various undesirable conditions of finger and sensor, the proposed CNN model consists of two streams that reconstruct the fingerprint image and orientation field simultaneously. The enhanced fingerprint is further refined using the orientation field information. Moreover, we create a deliberately corrupted fingerprint image dataset associated with ground truth images to facilitate the supervised learning of the proposed CNN model. Experimental results show significant improvement on both image quality and fingerprint matching accuracy after applying the proposed fingerprint image enhancement technique to several well-known fingerprint datasets.

Evaluating biometrics fingerprint template protection for an emergency situation

Biometric template protection approaches have been developed to secure the biometric templates against image reconstruction on the stored templates. Two cancellable fingerprint template protection approaches namely minutiae-based bit-string cancellable fingerprint template and modified minutiae-based bit-string cancellable fingerprint template, are selected to be evaluated. Both approaches include the geometric information of the fingerprint into the extracted minutiae. Six modified fingerprint data sets are derived from the original fingerprint images in FVC2002DB1_B and FVC2002DB2_B by conducting the rotation and changing the quality of original fingerprint images according to the environment conditions during an emergency situation such as wet or dry fingers and disoriented angle of fingerprint images. The experimental results show that the modified minutiae-based bit-string cancellable fingerprint template performs well on all conditions during an emergency situation by achieving the matching accuracy between 83% and 100% on FVC2002DB1_B data set and between 99% and 100% on FVC2002DB2_B data set.

Fingerprint Image De-Noising Using Wavelet Transform with the Comparison of Filtered and After Compression Filtered Noise Image

Fingerprint is becoming the part of our day to day life right from our home to workplace. Now a days for security and safety purpose prime importance is given by it. Also, Fingerprint identification is one of the most popular biometric technologies and which is highly used in criminal investigations, commercial applications, and so on. The performance of a fingerprint image-matching algorithm depends heavily on the quality of the input fingerprint images. It is very important to acquire good quality images. The use of wavelet transform improves the quality of an image and reduces noise level. So, in this research, different compression techniques are used to overcome this problem. Also, we have used different wavelets transformation for compression of fingerprint images. Image quality before compression and after compression are measured by Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR). This work is done in MATLAB using DSP and wavelet toolbox. At last, we have compared the filtered noise image method and the compression filtered noise image method.

Latent Fingerprint Identification Using Deep Learning Method

Digital fingerprint is one of the most consistent modalities in up to date biometrics and hence has been broadly studied and deploy in real applications. The accuracy of one Automatic Fingerprint Identification System (AFIS) largely depends on the quality of fingerprint samples, as it has an important impact on the degradation of the matching (comparison) error rates. This thesis generally focuses on the evaluation of biometric quality metrics and Fingerprint Quality Assessment (FQA), particularly in estimating the quality of gray-level latent fingerprint images or represented by minutiae set. By making a refined review of both biometric systems and relevant evaluation techniques, this contribute by the definition of a new evaluation or validation outline for estimating the performance of biometric quality metrics. It is defined to check the quality of latent fingerprint images by statistically measured parameters. In this work, an automatic Region-Of-Interest (ROI)-based latent fingerprint quality assessment technique is proposed by using deep learning. The first stage in our model uses deep learning, namely Region Convolutional Neural Network (R-CNN) to segment a latent fingerprint. In the second stage, feature vectors computed from the segmented latent fingerprint are used as input to a multi-class perceptron that predicts the value of the fingerprint. This proposed approach eliminates the need for manual ROI and feature markup by dormant examiners. Finally, experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Fingerprint recognition techniques are dependent on the quality of fingerprint images. An efficient enhancement algorithm improves the performance of recognition algorithms for poor quality images. Performance improvement of the recognition algorithms will be more if the enhancement process is adaptive to the fingerprint qualities (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment (FQA) algorithm assigns the appropriate quality class of dry, wet, normal dry, normal wet, and good quality using Fuzzy C-means clustering technique to each fingerprint image. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity (RVAU), and ridge valley uniformity (RVU) to cluster the fingerprint images into suitable quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement (FQE) process. In the first stage, a quality adaptive preprocessing (QAP) method is used to preprocess the fingerprint images. Next, fingerprint images are enhanced with Gabor, short-term Fourier transform (STFT), and oriented diffusion filtering (ODF) based enhancement techniques in the second stage. Experimental evaluations are performed on a quality driven database of FVC 2004. Results show that the performance improvement of 1.54% to 50.62% for NBIS matcher and 1.66% to 8.95% for VeriFinger matcher are achieved while the QAP based approaches are used in comparison to the current state-of-the-art enhancement techniques. In addition, the experimentation is also performed on FVC 2002 database to validate the robustness and efficacy of the proposed method.

The Enhancement of Fingerprint Images using Gabor Filter

Fingerprint is used as an object of the image that can be characteristic of every human being. Fingerprint images that have noise, blur and are not clearly a major problem in image processing. Enhancement of fingerprint image quality is proposed with the Gabor Filter method. The steps taken begin by increasing the local image contrast by applying the adaptive histogram equalization (AHE) method, then, corrected by the Gabor Filter and binarization methods. Image quality improvement was measured by peak signal to noise ratio (PNSR) and mean square error (MSE). The results of the experiment using the NIST dataset resulted in the best value of PNSR of 18.2692 and MSE of 159.3449.

Rancang Bangun Sistem Presensi Menggunakan Sidik Jari (Studi Kasus Pada Lembaga Pemasyarakatan Klas II-A Palu)

Dermatoglyphic has been widely applied to recognize a person's identity, because people basically have something unique or characteristic possessed only by themselves. This raises the idea of making it a unique identity. This is useful for employees presence applications, especially at Lembaga Pemasyarakatan Klas IIA Palu which is still doing manual presence. This system provides the division of labor time and wereable to detect the fingerprints of employees using a fingerprint scanner. The system was made in several stages of the input fingerprint image, binarization fingerprint image, fingerprint image quality improvement, and matching fingerprint image. Improvements to the quality of the fingerprint image consists of the surgical process of mathematical morphology opening and erosion. The detection process of the fingerprint image used euclidean distance measurement points minutiae. The test results indicate that the presence system accuracy using euclidean distance measurement could detect the fingerprint image by 89% from 20 fingerprint data with 5 times testing for each data.

Fingerprint image quality assessment and scoring using minutiae centered local patches

Performance of an automatic fingerprint identification system (AFIS) depends on the quality of fingerprint images. Therefore, quality estimation of fingerprint images can lead to performance enhancement of AFIS by eliminating the poor quality fingerprint images. A fingerprint quality estimation algorithm is proposed, which computes the fingerprint image quality at local level (blockwise). The proposed quality estimation algorithm analyzes blocks of fingerprint images in terms of their quality nature (dry, normal dry, good, normal wet, and wet). The features used for quality nature assessment are moisture, mean, variance, ridge valley area uniformity, and ridge line count. The performance of the block quality nature is assessed on fingerprint verification competition (FVC) 2004 datasets using a decision tree classifier. The proposed approach achieves classification accuracy of 95.90%. Further, the overall quality score (QS) for a fingerprint image is obtained by combining QSs assigned to all minutiae centered local patches of the fingerprint image using quality nature assessment, orientation analysis, and clarity analysis. The overall QSs for fingerprint images of FVC 2004 database (DB1, DB2, DB3, and DB4 datasets) are computed. These scores are used to evaluate the quality ranked recognition performance on each dataset of FVC 2004 database. Experimental evaluations reveal that rejecting low quality fingerprint images improves the performance of the recognition system. A comparative study with state-of-the-art quality estimation algorithms indicates that the QSs assigned using the proposed method are accurate and precise. Therefore, the proposed method can be used as a quality control unit during fingerprint acquisition, which helps in improving the performance of a recognition algorithm.

LQM: Localized Quality Measure for Fingerprint Image Enhancement

Fingerprint is one of the most widely used biometric in law enforcement. However, low-quality fingerprint images can drastically degrade the performance of automated fingerprint identification systems (AFIS). AFIS can be substantially advanced by: 1) establishing a metric to evaluate the image quality accurately and 2) utilizing this metric to enable an automated enhancement process. This paper offers a novel localized quality measure (LQM) to evaluate the quality of fingerprint images, and a genetic localized quality measure enhancement (LQME) algorithm, which is tailored to iteratively enhance poor-quality fingerprint images. In addition, a method is introduced to automatically choose the enhancement algorithm's parameters based on the proposed measure such that it yields the best enhancement result. The presented LQM measure uses fingerprint image characteristics, which include sharpness, contrast, orientation certainty level, symmetry features, and imprints of friction ridge structure (minutiae) information. The FVC2004 Set B database containing fingerprint images from four different sensors and a total of 240 images (80 from each sensor) is used to evaluate the performance of the presented algorithms and methods. The computer simulations demonstrate that the LQM measure is useful in predicting the quality of the fingerprint images captured from various devices. Furthermore, the experiments show that LQME can recover retrievable-corrupt fingerprint regions.

The Effects of High Dynamic Range on Fingerprint Images Processed by Photoshop.

High dynamic range (HDR) imaging is a function that combines five images with different exposures into a single image. This technique may provide fine ridge details of fingerprint images for forensic latent fingerprint examination. Therefore, viewing fingerprints under optimal conditions is of paramount importance. This paper analyzes HDR and non-HDR photos by using the Michelson contrast formula. The Michelson formula will provide a measurement to determine whether better contrast between the background and print can be achieved using the HDR function and if the background color affects the quality of the images. Two hypothesis were tested: (i) the HDR image provides more details of fingerprints with a better tone, greater clarity, and contrast than a normally exposed image regardless of the background color; (ii) the background color does not affect the quality of HDR fingerprint images overall, but the multi-color background may increase the contrast of HDR fingerprint images in some cases.

Combining multi-scale composite windows with hierarchical smoothing strategy for fingerprint orientation field computation

BackgroundOrientation field (OF) plays a very significant role in automatic fingerprint recognition systems. Many algorithms have been proposed for the estimation of fingerprints’ OF but it is hard to solve the dilemma of correcting spurious ridge structure and avoiding singularity location deviation, especially for poor images. So far, the following drawbacks still need to be solved for OF construction methods for practical application: (1) How to adaptively choose block scales to resolve the contradiction between accuracy and anti-noise, since small scale is beneficial to accuracy but is sensitive to noise, while large scale is more resistant to noise, but the accuracy is deteriorated. (2) How to construct the genuine OF in the areas close-by singular points and to evade singularity location deviation? Current block based methods give spurious OF estimates in the area near singular points because these areas have large curvature thus the detected singular points deviate from the genuine localizations. When these singular points are used as the anchor for referencing minutiae, it makes the average error of matching or recognition even larger. Therefore, it is essentials to construct the genuine OF in the areas close-by singular points and to evade singularity deviation.MethodsTo overcome the above-mentioned limitations, a novel method, combining a weighted multi-scale composite window (WMCM) with a hierarchical smoothing strategy has been proposed for the computation of fingerprint OF. This method mainly contains two procedures: the approximate OF estimation and the hierarchical OF smoothing. In the first procedure, a series of OFs are established under multiple scales of composite windows by using a gradient based method then a coarse OF is estimated using the weight of each scale determined by a squared gradient consistency. In the second procedure, the OF is first quantized into a two-digitized orientation zone and a two-orientation-zone filtering strategy is adapted to the OF blocks based on a filtering mask obtained after eliminating the isolated blocks. In the end a similar three-digitized orientation zone is performed to obtain an accurate and smooth OF. To validate the performance, the proposed method has been applied to OF computation using the FVC2004 databases and three experiments are designed. Experiment 1 aims to validate whether the weighted multi-scale composite window can balance the dilemma of accuracy and robustness more effectively than the previous works do. Experiment 2 is designed to examine whether the hierarchical smoothing method can correct the spurious ridge flow and preserve the genuine localization of singular points. The purpose of experiment 3 is to test the performance of the proposed method on OF reconstruction in low quality fingerprint images. The fingerprint databases FVC 2004 DB1–DB4 are employed in this study.ResultsThe results of experiment I shows that the proposed method is capable to extract the information of OF reliably and it is more robust against singularity localization deviation in comparison with the other three gradient based methods. The results of experiment II indicates that the proposed smoothing method can balance the contradiction in correcting spurious ridge structures and preserving genuine singularity localization. The results of experiment III illustrates that our approach combing WMCW with the hierarchical smoothing method is capable to extract the information of OF ridge reliably and it is more robust against singularity deviation in comparison with the other three gradient based methods. In a word, the experiment results demonstrate that the proposed method can correct spurious ridge structure and meanwhile avoid singularity deviation compared with the previous works.ConclusionsA novel gradient based algorithm has been proposed which is more reliable for the estimation of the ridge information for fingerprint OF and is more accurate in preserving the singularity localization. Compared with the previously proposed gradient based methods, the advantages of the proposed RBSF lie in three aspects. Firstly a weighted multi-scale composite window is put forward to replace the single window used by conventional gradient based methods and to adaptively choose the scales of the blocks. Secondly, a hierarchical smoothing strategy is proposed to enhance the OF by using the two-orientation-zone filtering and the three-orientation-zone filtering, aiming to correct the spurious ridges and preserving the genuine location of singular points. Finally, three experiments are designed to test the proposed algorithm together with other popular gradient based methods on real fingerprint images, which are selected from different categories and all are suffering from obvious noise effects. All the experiment results show that the proposed method is superior with respect to reliable OF construction and avoiding singularity localization deviation.

Latent Fingerprint Enhancement Based on Directional Total Variation Model with Lost Minutia Reconstruction

Image enhancement plays an important role in biometric systems, this paper presented automatic latent fingerprint segmentation and matching. While considerable progress has made in both rolled and plain fingerprint image enhancement, latent fingerprint enhancement is a challenging problem due to the poor image quality of latent fingerprint with unclear ridge structures and various overlapping patterns, along with the presence of structured noise. Prior to latent fingerprint segmentation and feature extraction, latent fingerprint image enhancement is important to suppress various types of noise and to clarify the ridge structure. This paper reviews the current techniques used for latent fingerprint enhancement and presents a hybrid model which combines the edge directional total variation model (EDTV) and quality image enhancement with lost minutia reconstruction. The NIST SD27 database is used to test the performance of the proposed techniques with RMSE and PSNR. The proposed technique is effectively clarify input latent fingerprint images and eliminate noise in good, bad and ugly latent fingerprint images. A statistically significant difference, which focused on the mean lengths of PSNR and RMSE for different categories of latent fingerprint, images (good, bad and ugly). The proposed technique performs well for the good latent fingerprint images compare to bad and ugly images. Enhancement respectively presents RMSE averages of 0.018373, 0.022287, and 0.023199 for the good, bad and ugly image SD27 image set, as opposed to 82.99068, 81.39749, and 81.07826 for PSNR. The proposed enhancement technique improved the matching accuracy of latent fingerprint images by about 30%.