Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis

Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints,which are made of common fingerprint materials,such as silicon,latex,etc.Thus,to protect our privacy,many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint.Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features,but these methods normally destroy or lose spatial information between pixels.Different from existing methods,convolutional neural network(CNN)can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled data.Thus,CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this paper.To reduce the redundant information and extract the most distinct features,ROI and PCA operations are performed for learned features of convolutional layer or pooling layer.After that,the extracted features are fed into SVM classifier.Experimental results based on the LivDet(2013)and the LivDet(2011)datasets,which are captured by using different fingerprint materials,indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.

Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

A simple chemical method for the synthesis of Cu^(2+) engrafted MgAl_(2)O_(4) nanoparticles: Efficient fluoride adsorbents, photocatalyst and latent fingerprint detection

An adaptable, energy efficient chemical process is employed to synthesize Cu^2+engrafted MgAl2O4 nanoparticles(Mg1-xCuxAl2O4, x = 0, 0.1, 0.3, 0.5 abbreviated as MCA0, MCA1, MCA3,and MCA5 respectively), using chelating ligand and the calcination temperature was determined by the thermogravimetric analysis of the precursor mass.They acted as good fluoride adsorbent in the presence of co-ions, different pH(2–11) via chemisorption revealed from Fourier-transform infrared spectroscopy(FTIR) and photodegraded Methylene Blue(MB).The satisfactory results were for MCA1(specific surface area 25.05 m^2/g) with 97%fluoride removal at pH 7.0 for the 10 mg/L initial fluoride concentration for 1.5 g/L adsorbent dose with 45 min contact time obeying the Langmuir isotherm model with negative thermodynamic parameters and 4 mmol of MCA3 with 98.51% photodegradation for 10-5 mol/L MB solution obeying pseudo-second-order and pseudo-first-order kinetics respectively.The proposed photodegradation mechanism of MB was established by the FTIR and high-performance liquid chromatography(HPLC) analysis.The nanoparticles are cubic, estimated through X-ray diffraction(XRD) and transmission electron microscopy(TEM) analysis.The band gap energies, grain size, and the effective working pH were estimated by diffuse reflectance spectra(DRS), scanning electron microscope(SEM), and zero-point potential analysis respectively.A soil candle with MCA1 also fabricated for the household purpose and tested with some fluorinated field samples.The MCA3 was able to enhance the latent fingerprint on smooth surfaces.

Red, green and blue aggregation-induced emissive carbon dots

As one of the most promising fluorescent nanomaterials, carbon dots(CDs) have been extensively studied for their fluorescent properties in solution. However, research on the synthesis of multicolor solid-state fluorescence(SSF) CDs(from blue to red) is rarely reported. Herein, we used o-phenylenediamine, mphenylenediamine and p-phenylenediamine with dithiosalicylic acid(DTSA) in the solvothermal reaction using acetic acid as a solvent to obtain aggregation-induced emissive(AIE) CDs of red(620 nm), green(520 nm), and blue(478 nm), respectively. XPS spectra and TEM image show that with the red-shift of luminescence, the particle size and content of C=O of the CDs gradually increases. Finally, based on the non-matrix solid-state multicolor luminescence characteristics of CDs, the application of white light LED devices is realized. Besides, based on the fat-soluble properties of CDs, fingerprint detection applications are realized.