Developing new materials that could identify fingerprint using the naked eye and observe the level 3 microscopic details is challenging. Here, we designed a novel hydrochromic and piezochromic dual-responsive optical film, which achieved the visual transparency transition. The performances of hydrochromic and piezochromic responses from high transparency to opaque whiteness were attributed to the introduction of poloxamer. The hygroscopic swelling of the disordered micelles led to light scattering, causing the hydrochromic response. The piezochromic response may be ascribed to the microcracks in the fragments of poloxamer crystals, which changed the refractive index of light. The fascinating combination of hydrochromic and piezochromic response was effectively applied in fingerprint identification. Hydrochromic response accurately recognized sweat pores, and piezochromic response could gradually reveal the ridges and valleys according to the different color of imprinted fingerprints. The film could identify fake fingerprints based on the differences in sweat pores between fake fingerprints and living fingers. More importantly, the film could easily detected not only the clear ridges but also the detailed sweat pores using the naked eye, indicating that the film has profound research significance in fingerprint analysis and liveness fingerprint detection.

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, which results in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking with worn fingerprints. The research findings indicated that the traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, the traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D-printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on the unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.

Fingerprints hold evidential value for individual identification; a sensitive, efficient, and convenient method for visualizing latent fingermarks (LFMs) is of great importance in the field of crime scene investigation. In this study, we proposed an aggregation-induced emission atomization technique (AIE-AT) to obtain high-quality fingermark images. Six volunteers made over 1566 fingerprint samples on 17 different objects. The quality of fingermark development was evaluated using grayscale analysis for quantitative assessment, combining the fluency of fingermark ridges and the degree of level 2 and level 3 features. Both qualitative and quantitative methods were employed to explore the effectiveness of AIE molecule C27H19N3SO in developing fingermarks, its applicability to objects, and its individual selectivity. Additionally, the stability of the AIE molecule was examined. Comparative experimental results demonstrated the high stability of the AIE molecule, making it suitable for long-term preservation. The grayscale ratio of the ridges and furrows was at least 2, with high brightness contrast, the level 2 and level 3 features were clearly observable. The AIE-AT proved to be effective for developing fingermarks on nonporous, porous, and semiporous objects. It exhibited low selectivity on suspects who leave fingermarks and showed better development effects on challenging objects, as well as efficient extraction capability for in situ fingermarks. In summary, AIE-AT can efficiently develop latent fingermarks on common objects and even challenging ones. It locates the latent fingermarks for further accurate extraction of touch exfoliated cells in situ, providing technical support for the visualization of fingermarks and the localization for extraction of touch DNA.

The analysis of fingerprint chemical composition is a meaningful way to excavate the multidimensional information of fingerprint, including the donor profiling information and the age of a fingerprint, which broadens the evidential values of fingerprint, especially for the partial and distorted fingerprint. But the research remains still in the pilot phases or is ongoing. Amino acids are the dominant organic substances in latent sweat fingerprint and influenced by many donor factors. Hence, their content reflects personal information of donors. Forensic science will be revolutionized if suspects can be individualized by their amino acid content. The diverse nature, distinct physicochemical properties, and ultra-micro levels of amino acids present in fingerprints make it hard to detect. A high sensitivity method for detecting and quantifying multiple amino acid components is required. UHPLC-QqQ MS/MS offers high sensitivity, high separation, simultaneous multicomponents detection, and no derivatization, making it an ideal method for detecting and analyzing amino acids in fingerprints. Therefore, in this study, we propose and validate an efficient UHPLC-QqQ MS/MS method for the extraction and analysis of 13 amino acids from fingerprint. We compared the results of amino acids of 10 different substrates and found that the inherent amino acids in most porous substrates would have been extracted along with the fingerprint amino acids, making them unsuitable for quantitative amino acid analysis. Instead, plastic sheets are ideal substrates for laboratory studies. Then, extensive experiments were conducted among 30 donors for multidimensional information analysis. The type of samples analyzed were eccrine-rich fingerprints. A Binary Logistic Regression (BLR) model was developed, and the female and male donors were successfully differentiated by amino acids in fingerprints. Two other mathematical models were also developed to verify the accuracy, and all three different mathematical models were able to identify donors of different genders with over 90% accuracy. This demonstrates that amino acids have the potential to provide more information for donors as metabolic markers. In the future, we will conduct a series of experiments to analyze more multidimensional information for individual identification by amino acid content in the fingerprint.

The exploration of multifunctional materials and intelligent technologies used for fluorescence sensing and latent fingerprint (LFP) identification is a research hotspot of material science. In this study, an emerging crystalline luminescent material, Eu3+-functionalized hydrogen-bonded organic framework (Eu@HOF-BTB, Eu@1), is fabricated successfully. Eu@1 can emit purple red fluorescence with a high photoluminescence quantum yield of 36.82%. Combined with artificial intelligence (AI) algorithms including support vector machine, principal component analysis, and hierarchical clustering analysis, Eu@1 as a sensor can concurrently distinguish two carcinogens, roxarsone and aristolochic acid, based on different mechanisms. The sensing process exhibits high selectivity, high efficiency, and excellent anti-interference. Meanwhile, Eu@1 is also an excellent eikonogen for LFP identification with high-resolution and high-contrast. Based on an automatic fingerprint identification system, the simultaneous differentiation of two fingerprint images is achieved. Moreover, a simulation experiment of criminal arrest is conducted. By virtue of the Alexnet-based fingerprint analysis platform of AI, unknown LFPs can be compared with a database to identify the criminal within one second with over 90% recognition accuracy. With AI technology, HOFs are applied for the first time in the LFP identification field, which provides a new material and solution for investigators to track criminal clues and handle cases efficiently.

A facile and high-resolution enhancement of latent fingerprints (LFPs) has been developed by using a wet nitrocellulose (NC) membrane as a matrix under natural light. A clear fingerprint pattern was presented on the membrane after a fingertip touch owing to the difference in light transmittance between the ridge residues and the wet-NC-membrane background. Compared with conventional methods, this protocol can provide a higher resolution fingerprint image to extract level 3 details accurately. It is also compatible with commonly used fingerprint visualization techniques (magnetic ferric oxide powder and AgNO3. The modified membrane could be more general to realize the high-resolution visualization of LFP transferred from various substrates, even independent of light projection. Due to the excellent feasibility and reproducibility of level 3 details extracted by the wet NC membrane, the frequency distribution of the distance between adjacent sweat pores (FDDasp) could be used to effectively distinguish the fragmentary fingerprints. Finally, the level 3 features of LFPs from females and males were conveniently extracted by the wet-NC-membrane method for gender identification. The statistical results indicated that females had a higher average sweat pore density (115/9 mm2) than males (84/9 mm2). Taken together, this approach provided a high-resolution, reproducible, and accurate imaging of LFPs, which shows great promise for forensic information analysis.

The Daubert case in Philadelphia in 1999 caused a debate about the scientificity of fingerprint evidence. Since then, the current fingerprint identification system has been constantly challenged and questioned. Quantitative identification technology based on the statistics of fingerprint minutiae has become a new research hot spot. In this paper, an automatic detection algorithm is designed to achieve automatic classification of fingerprint minutiae using the deep convolution neural network YOLOv5 model. Then the occurrence frequencies of minutiae are statistically evaluated in 619,297 fingerprint images. The results show that the frequency ranges (unit%) of six types of minutiae per finger are ridge endings [68.49, 70.81], bifurcations [26.37, 27.26], independent ridges [1.533, 1.626], spurs [1.129, 1.198], lakes [0.4588, 0.4963], crossovers [0.3034, 0.3256]. The results also show that there are differences in the distribution frequency of the six types of minutiae in the ten finger positions ( thumb, middle, ring, index and little finger of the left and right hand) and in the four finger patterns ( arch, left loop, right loop and whorl). From the quantitative point of view of fingerprint identification, this paper calculates the number and frequency ranges of six types of minutiae, distinguishes the evaluation value of each type of minutiae, and provides the basic data support for establishing a probability model of fingerprint identification in the future.

The local regional similarity of fingerprints has always been a hot issue in the field of fingerprint research. With the increasing size of ten-print databases, the appearance of close non-matches (CNMs) in automatic fingerprint identification system (AFIS) candidate lists has attracted increasing attention from forensic science departments worldwide. In this study, three categories (high-, medium-, and low-level) of standards for CNMs were established and 60 whorl samples were marked with different numbers of minutiae to explore the occurrence and influencing factors of CNMs in AFIS candidate lists based on a ten million people database. The results showed that all prints could be found with their corresponding CNMs. The average occurrence rate of CNMs for every query was 52.7% in the top 100 lists, and the most similar CNM was exactly the same in the local area of 12 coincidence points. CNMs appeared more in the middle and lower parts of the central region of the whorl. Moreover, shorter C2C distances and the same finger number and hand led to more CNMs being inspected. CNMs with higher similarity required a more extensive regional area and smaller minutiae density. We concluded that CNMs have a high occurrence rate in large-scale databases and many factors are closely related to them. Fingerprint examiners and researchers need to strengthen their understanding of CNMs to avoid the occurrence of misidentification like the Madrid bombings.

The blood fingerprint enhancement is not so eye-catching as latent fingerprint development in forensic community, but it is indeed an important piece of evidence for personal identification, forensic analysis and even reconstruction of crime scenes. In over past ten years, novel reagents, advanced materials and emerging techniques have growingly participated in blood fingerprint enhancement, which not only leads to a higher level of developing sensitivity, selectivity and contrast, but also endows blood impressions with more forensic significance. This review summarizes recent advances in conventional chemical reagents targeting at heme, protein and amino acid as well as emerging enhancement techniques based on advanced materials, new equipment or methods. Some critical issues in forensic science are also discussed, including partial blood fingerprint enhancement, false positive of developing reagents, the compatibility of blood enhancement technique and DNA, fingerprint age determination, and so on. Finally, we have proposed several urgent problems to be solved and the prospects of some promising techniques were proposed in the field of blood fingerprint enhancement in future work.

Fingerprints provide sufficient and reliable discriminative characteristics which have been considered one of the most robust evidence for individualization. The limitation of current minutiae-based fingerprint technology seems to be solved with the development of level 3 features since they can offer additional information for problematic fingerprint recognition and even donor profiling. So far, tremendous efforts have been devoted to detecting and analysing the third-level details. This review summarizes the advances in level 3 details with an emphasis on their reliability assessment, visualization methods based on physical interaction, residue-response, mass spectrometry and electrochemical techniques, as well as the potentiality for individualization, donor profiling and even other application scenarios. In the end, we also give a personal perspective on the future direction and the remaining challenges in the third-level-detail-related field. We believe that the new exciting progress is expected in the development of level 3 detail detection and analysis with continued interest and attention to this field.