Clear Fingerprint Images Research Articles

Strongly Fluorescent Blue-Emitting La2O3: Bi3+ Phosphor for Latent Fingerprint Detection.

Blue-emitting bismuth-doped lanthanum oxide (La2O3: Bi3+) with various concentrations of Bi was synthesized using the sol-gel combustion method and used for visualization of latent fingerprints (LFPs). An X-ray diffraction (XRD) study revealed the hexagonal structure of the phosphors and total incorporation of the bismuth in the La2O3 matrix. Field Emission Scanning Electron Microscopy (FE-SEM) and Fourier Transform Infrared Spectroscopy (FTIR) were used to study the morphology and the relative vibrations of the synthesized samples. Photoluminescence (PL) studies showed strong blue emission around 460 nm due to the 3P1 → 1S0 transition. Clear bright-blue fingerprint images were obtained with the powder dusting method on various surfaces like aluminum, compact discs, glass, wood and marble. A first evaluation of these images indicated a clear visualization of all three levels of details and a very high contrast ranging from 0.41 on marble to 0.90 on aluminum. As a further step, we used an algorithm for extracting fingerprint minutiae with which we succeeded in detecting all three levels of fingerprint details and even the most difficult ones, like open and closed pores. According to these analyses, La2O3: Bi phosphor is demonstrated to be an effective blue fluorescent powder for excellent visualization of latent fingerprints.

Fingerprint classification combined with Gabor filter and convolutional neural network

A fingerprint is an impression left by the friction ridges of a human finger. A fingerprint classification system groups fingerprint according to their characteristics and therefore helps to match a fingerprint against an extensive database of fingerprints. The Henry classification system is widely used among fingerprint classification systems. Some researchers have used traditional machine learning or deep learning for fingerprint classification. Nevertheless, traditional algorithms cannot extract the depth features of the fingerprint, and most deep learning algorithms lack fingerprint image enhancement. So, this paper combined the Gabor Filter and Convolutional Neural Network to extract fingerprint features. The model has two channels, one is a Deep Convolutional Neural Network (DCNN), and the other is a Shallow Convolutional Neural Network (SCNN). The DCNN consists of a neural network with eight layers, which can extract deep features of the fingerprint. The SCNN consists of Gabor Filter and a neural network with two layers that can extract features from clear fingerprint images. This paper uses NIST Special Database 4 for experiments. Experimental results show that the model proposed in this paper has achieved 91.4% accuracy. Compared with other algorithms, this model has higher accuracy than others. It shows that combined with the Gabor Filter and Convolutional Neural Network can better extract the ridge features of fingerprint images.

Chemical Imaging of Fingerprint on RMB Banknotes Using Time-of-Flight Secondary Ion Mass Spectrometry

The development of latent fingerprints on long-circulated RMB banknotes has been one of the challenging task in forensic science research. In this work, time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used to perform chemical imaging of latent fingerprints on the surface of the substrate due to its advantages such as in-situ, quasi-nondestructive, high-throughput, simultaneous analysis of multi-components and high spatial resolution. The developing effect of TOF-SIMS was firstly compared with that of several traditional methods, and the reasons for the unique advantages of TOF-SIMS were discussed. Its applicability was further discussed from the perspective of universality, sensitivity and fingerprint three-level feature acquisition capability. Finally, exogenous substances in fingerprints were also analyzed by using this technology to detect the molecular ions (m/z 92.06) and fragment ion peaks (m/z 43.02) of glycerol in the moisture lotion and developed clear fingerprint images. Preliminary research results showed that TOF-SIMS technology could provide an effective method for the development of latent fingerprints on long-circulated RMB banknotes and was expected to be used for the detection of substances in fingerprints on the surface of various substrates.

Detection of fingerprints on porous papers and performance evaluation

At crime scenes, fingerprints are often found on paper. The visualization of invisible fingerprints, so-called latent fingerprints (LFPs), is critical in forensic investigation. Laser-induced fluorescence technology for fingerprint detection has the advantages of sensibility, high-resolution, non-destructiveness and non-contact. In this paper, the characteristics of commonly used paper and the interaction between ultraviolet light and porous paper are briefly analyzed firstly. Next the fingerprint detection device is introduced. Then the output energy of the laser is optimized and a series of experiments are carried out. Local gray value stretching and power enhancement algorithms are used to process the fingerprint fluorescence data, and the high contrast fingerprint images are obtained. The experimental results show that the fingerprint substance and paper are degraded in different degrees under the excitation of ultraviolet light, and the second or third detection can improve the signal-to-noise ratio of detection. By the method of this paper, clear fingerprint images can be obtained quickly, and more than 10 secondary fingerprint structures can be extracted from these fingerprint images, which provides quantitative evidences for criminal investigation.

P‐215: Organic‐Inorganic Hybrid Thin‐film Photo‐detector for Fingerprint Recognition

A near‐IR based optical type fingerprint sensor is designed and implemented. The driving system is based on the passive pixel sensor (PPS) array with Oxide (IGZO) TFT technology. The sensing element of the optical sensor is composed of the organic photosensitive diode (OPD) exhibited typical sandwich structure. Clear fingerprint images can be successfully captured by such organic‐inorganic hybrid thin‐film photo‐detector. Further, flexible fingerprint sensor is also realized based on PI substrate.

Fluorescent Cyanoacrylate Monomers and Polymers for Fingermark Development

Cyanoacrylate esters with fluorescent side groups were synthesized and tested as agents for latent fingerprint development. Reactive monomers with benzyl, anthracyl, naphthyl, fluorenyl, propagyl, and cyanomethyl side groups were synthesized using the formation of an ethyl cyanoacrylate, anthracene adduct, followed by hydrolysis of the ethyl ester to the acid and esterification with a desired alcohol, and finally release of the monomer by retro-Diels–Alder with maleic anhydride. Monomers were prepared in high yield and purity as determined by spectral analysis. Attempts to synthesize these monomers from poly(ethyl cyanoacrylate) by transesterification and depolymerization resulted in low yields and low purity. The synthesized fluorescent monomers were found to be effective for latent fingerprint development in solution forming clear fluorescent fingerprint images suitable for forensic fingerprint comparison. These monomers can complement the current use of the commonly used nonfluorescent ethyl cyanoacrylate monomers for fingerprint development.

Capacitive-Sensing Circuit Technique for Image Quality Improvement on Fingerprint Sensor LSIs

This paper describes a new capacitive-sensing circuit technique that improves the quality of images captured with capacitive fingerprint sensor LSIs. The quality of the captured image depends on the surface condition of the finger. When the finger is dry, the electrical resistance of the finger surface is high. The finger surface resistance induces a voltage drop in the electrical potential of the finger surface (which should be grounded), which leads to poor image quality. To capture clear images even when the finger is dry, the circuit technique improves the image quality using the series resistance caused at the finger surface. The potential of the finger surface is controlled by an enhancement plate and a voltage control circuit. A test chip implementing this technique was fabricated on a 0.5 ?m CMOS process and a sensor process. The chip captures a clear fingerprint image of a dry finger, confirming the effectiveness of the circuit technique for capturing clear fingerprint images independent of the finger surface condition with the capacitive fingerprint sensor LSIs.

Novel Surface Structure and Its Fabrication Process for MEMS Fingerprint Sensor

This paper describes a microelectromechanical systems (MEMS) fingerprint sensor that has novel protrusions on the sensor surface in order to detect clear fingerprint images for various finger surface conditions. A preliminary experiment clarified the importance of contact between soft finger ridges and the sensor surface in producing fingerprint images. Based on this, novel T-shaped protrusions on the sensor surface are proposed as an interface to mediate the contact. In order to fabricate the overhung form of the T-shaped structure, photosensitive polyimide and a copper sacrificial layer were used. Using the CMOS-compatible MEMS fabrication process, the arrayed T-shaped protrusions of polyimide are stacked on a CMOS large-scale integrated circuit. The sensor produced clear fingerprint images for various kinds of fingers. Measurement of the relationship between applied force and sensor output showed that the T-shaped protrusions achieved high sensitivity regardless of finger elasticity. Reliability tests confirmed that the sensor with the T-shaped protrusions has sufficient mechanical and electrical strength for wide applications.

FINGERPRINT ENHANCEMENT USING WAVELET TRANSFORM COMBINED WITH GABOR FILTER

The performance of automatic fingerprint identification system (AFIS) is heavily determined by the quality of the input image, thus an effective method to enhance the fingerprint image is essential in such a system. In this paper, we combine the filter-based method, which is mostly used nowadays with wavelet transform to achieve a more reliable and effective approach to fingerprint enhancement. This novel approach consists of five main steps, namely: (1) normalization, (2) decomposition, (3) wavelet coefficient adjustment, (4) Gabor filtering, and (5) reconstruction. Using this new method, a more clear fingerprint image can be obtained, which can distinctly improve the accuracy of the minutiae extraction module and finally achieve a better performance of the entire system. Experiments have been conducted in our study and positive experimental results have been received, which show that the proposed combined method is more effective and robust than other existing methods such as the filter-based and direct gray-level approaches.

MEMS fingerprint sensor immune to various finger surface conditions

This paper describes a new fingerprint sensor that detects the topography of finger ridges and valleys. We propose a microelectromechanical systems (MEMS) cavity structure for the pixels arrayed on the sensor surface and a fabrication process that stacks the cavity structures on a CMOS LSI. A thin film on top of the cavity structures is bent by finger ridges mechanically, which is detected by the sensing circuits below them electronically. Based on an analytical model, we designed a cavity structure suitable for fingerprint detection. We fabricated the cavity structures on the sensing circuits by a fabrication process that entails gold electroplating, sacrificial layer etching for cavities, and a sealing technique. The fabricated fingerprint sensor consists of about 57 334 pixels in the area of 11.2 mm /spl times/ 12.8 mm, which yields a high spatial resolution of 508 dots-per-inch (dpi). With these pixels working together, fingerprint images were obtained. The sensor produces clear fingerprint images regardless whether the finger was dry or wet, which confirms its potential for various practical applications.

A pixel-level automatic calibration circuit scheme for capacitive fingerprint sensor LSIs

We propose a pixel-level automatic calibration circuit scheme that initializes a capacitive fingerprint sensor LSI to eliminate the influence of the surface condition, which is degraded by dirt during long-time use. The scheme consists of an automatic calibration circuit for each pixel and a calibration control circuit for the pixel array. The calibration is executed by adjusting variable capacitance in each pixel to make the sensor signals of all pixels the same. The calibration control circuit selects the pixels in parallel, and calibrates all pixels in a short time. The scheme was applied to a fingerprint sensor LSI using the 0.5-/spl mu/m CMOS process/sensor process, and clear fingerprint images were obtained even for a degraded surface condition. This confirms that the scheme is effective for capturing consistent clear images during long-time use.

Characteristics of Fingerprint Sensing on Capacitive Fingerprint Sensor LSIs with a Grounded Wall Structure

This paper describes the dependence of fingerprint-sensing characteristics on sensed and parasitic capacitances. The sensed capacitance is formed between the sensor plate covered with a sensing insulator and the finger surface, which contacts the fingerprint sensor LSI. The parasitic capacitance is mainly formed on the grounded (GND) wall and metal layer under the sensor plate through the dielectric film. To investigate the dependence, we propose a circuit model of the GND wall structure that takes these capacitances into consideration. We analyzed the output dynamic range of the sensing circuit by varying the sensed and parasitic capacitances, which were changed by the sensor plate size and thickness of the sensing insulator and the dielectric film. The analysis and experimental results revealed that there are an optimal sensor plate size and suitable thickness of the sensing insulator and the dielectric film to effectively detect the sensed capacitance. These findings enable us to design the most effective GND wall structure to capture clear fingerprint images.

Evaluation of Sensitivity on a Capacitive Fingerprint Sensor LSI with a Grounded Wall Structure

This paper describes the sensitivity to capture fingerprint images on a capacitive fingerprint sensor LSI with a grounded (GND) wall structure. We investigated the sensing characteristics of the fingerprint sensor LSI with the GND wall structure in comparison with that of the conventional planar structure. For the analysis, we proposed circuit models based on the GND wall and the planar structure that take account of the finger's resistance, and calculated numerically the sensing characteristics. The analysis revealed that the resistance of the finger degrades the sensitivity and the GND wall structure provides a high sensitivity because it suppresses the finger's resistance. The experimental results confirmed that the proposed models are appropriate and clarified the effect of the GND wall structure on fingerprint images. Therefore, it is concluded that a fingerprint sensor LSI with the GND wall structure achieves a high sensitivity in capturing clear fingerprint images.