The fingerprint verification system is widely used in mobile devices because of fingerprint's distinctive features and ease of capture. Typically, mobile devices utilize small sensors, which have limited area, to capture fingerprint. Meanwhile, conventional fingerprint feature extraction methods need detailed fingerprint information, which is unsuitable for those small sensors. This paper proposes a novel fingerprint verification method for small area sensors based on deep learning. A systematic method combines deep convolutional neural network (DCNN) in a Siamese network for feature extraction and XGBoost for fingerprint similarity training. In addition, a padding technique also introduced to avoid wraparound error problem. Experimental results show that the method achieves an improved accuracy of 66.6% and 22.6% in the FingerPassDB7 and FVC2006DB1B dataset, respectively, compared to the existing methods.

This paper proposes statistical analysis of phase-only correlation (POC) functions under the phase fluctuation of signals due to additive Gaussian noise. We derive probability density function of phase-spectrum differences between original signal and its noise-corrupted signal with additive Gaussian noise. Furthermore, we evaluate the expectation and variance of the POC functions between these two signals. As the variance of Gaussian noise increases, the expectation of the peak of the POC function monotonically decreases and variance of the POC function monotonically increases. These results mathematically guarantee the validity of the POC functions used for similarity measure in matching techniques.

This paper proposes statistical analysis of phase-only correlation functions between two signals with stochastic phase-spectra following bivariate circular probability distributions based on directional statistics. We give general expressions for the expectation and variance of phase-only correlation functions in terms of joint characteristic functions of the bivariate circular probability density function. In particular, if we assume bivariate wrapped distributions for the phase-spectra, we obtain exactly the same results between in case of a bivariate linear distribution and its corresponding bivariate wrapped distribution.

Access control and surveillance applications like walking-through security gates and immigration control points have a great demand for convenient and accurate biometric recognition in unconstrained scenarios with low user cooperation. The periocular region, which is a relatively new biometric trait, has been attracting much attention for recognition of an individual in such scenarios. This paper proposes a periocular recognition method that combines Phase-Based Correspondence Matching (PB-CM) with a texture enhancement technique. PB-CM has demonstrated high recognition performance in other biometric traits, e.g., face, palmprint and finger-knuckle-print. However, a major limitation for periocular region is that the performance of PB-CM degrades when the periocular skin has poor texture. We address this problem by applying texture enhancement and found out that variance normalization of texture significantly improves the performance of periocular recognition using PB-CM. Experimental evaluation using three public databases demonstrates the advantage of the proposed method compared with conventional methods.

This letter proposes performance evaluation of phase-only correlation (POC) functions using signal-to-noise ratio (SNR) and peak-to-correlation energy (PCE). We derive the general expressions of SNR and PCE of the POC functions as correlation performance measures. SNR is expressed by simple fractional function of circular variance. PCE is simply given by squared peak value of the POC functions, and its expectation can be expressed in terms of circular variance.

This paper proposes the statistical analysis of phase-only correlation functions between two real signals with phase-spectrum differences. For real signals, their phase-spectrum differences have odd-symmetry with respect to frequency indices. We assume phase-spectrum differences between two signals to be random variables. We next derive the expectation and variance of the POC functions considering the odd-symmetry of the phase-spectrum differences. As a result, the expectation and variance of the POC functions can be expressed by characteristic functions or trigonometric moments of the phase-spectrum differences. Furthermore, it is shown that the peak value of the POC function monotonically decreases and the sidelobe values monotonically increase as the variance of the phase-spectrum differences increases.

This paper proposes statistical analysis of phase-only correlation functions with phase-spectrum differences following wrapped distributions. We first assume phase-spectrum differences between two signals to be random variables following a linear distribution. Next, based on directional statistics, we convert the linear distribution into a wrapped distribution by wrapping the linear distribution around the circumference of the unit circle. Finally, we derive general expressions of the expectation and variance of the POC functions with phase-spectrum differences following wrapped distributions. We obtain exactly the same expressions between a linear distribution and its corresponding wrapped distribution.

Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.

This paper proposes statistical analysis of phase-only correlation functions based on linear statistics and directional statistics. We derive the expectation and variance of the phase-only correlation functions assuming phase-spectrum differences of two input signals to be probability variables. We first assume linear probability distributions for the phase-spectrum differences. We next assume circular probability distributions for the phase-spectrum differences, considering phase-spectrum differences to be circular data. As a result, we can simply express the expectation and variance of phase-only correlation functions as linear and quadratic functions of circular variance of phase-spectrum differences, respectively.

This paper presents a faster one-dimensional (1-D) phase-only correlation (POC)-based method for estimations of translations, rotation, and scaling in images. The proposed method is to project two-dimensional (2-D) images horizontally and vertically onto 1-D signals, and uses 1-D POCs of the 1-D signals to estimate the translations in images. Combined with a log-polar transform, the proposed method is extended to scaling and rotation estimations. Compared with conventional 2-D and 1-D POC-based methods, the proposed method performs in a lower computational cost. Experimental results demonstrate that the proposed method is capable of estimating large translations, rotation and scaling in images, and its accuracy is comparable to those of the conventional POC-based methods. The experimental results also show that the computational cost of the proposed method is much lower than those of the conventional POC-based methods.

This paper presents a fast and accurate volume correspondence matching method using 3D Phase-Only Correlation (POC). The proposed method employs (i) a coarse-to-fine strategy using multi-scale volume pyramids for correspondence search and (ii) high-accuracy POC-based local block matching for finding dense volume correspondence with sub-voxel displacement accuracy. This paper also proposes its GPU implementation to achieve fast and practical computation of volume registration. Experimental evaluation shows that the proposed approach exhibits higher accuracy and lower computational cost compared with conventional method. We also demonstrate that the GPU implementation of the proposed method can align two volume data in several seconds, which is suitable for practical use in the image-guided radiation therapy.

This paper proposes a new method that reduces the computational cost of the phase-only correlation (POC)-based methods for displacement estimation in old film sequences. Conventional POC-based methods calculate all the points of the POC and only use the highest peak of the POC and its neighboring points to estimate the displacement with subpixel accuracy. Our proposed method reduces the computational cost by calculating the POC in a small region, instead of all the points of the POC. The proposed method combines a displacement pre-estimation with a modified inverse discrete Fourier transform (IDFT). The displacement pre-estimation uses the 1-D POCs of frame projections to pre-estimate the displacement with pixel accuracy and chooses a small region in the POC including the desired points for displacement estimation. The modified IDFT is then used to calculate the points in this small region for displacement estimation. Experimental results show that use of the proposed method can effectively reduce the computational cost of the POC-based methods without compromising the accuracy.

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

The recognition performance of the conventional 3D face recognition algorithm using ICP (Iterative Closest Point) is degraded for the 3D face data with expression changes. Addressing this problem, we consider the use of the expression-invariant local regions of a face. We find the expression-invariant regions through the distance analysis between 3D face data with the neutral expression and smile, and propose a robust 3D face recognition algorithm using passive stereo vision. We demonstrate efficient recognition performance of the proposed algorithm compared with the conventional ICP-based algorithm through the experiment using a stereo face image database which includes the face images with expression changes.

This paper proposes a three-dimensional (3D) face recognition system using passive stereo vision. So far, the reported 3D face recognition techniques have used active 3D measurement methods to capture high-quality 3D facial information. However, active methods employ structured illumination (structure projection, phase shift, moire topography, etc.) or laser scanning, which is not desirable in many human recognition applications. Addressing this problem, we propose a face recognition system that uses (i) passive stereo vision to capture 3D facial information and (ii) 3D matching using an ICP (Iterative Closest Point) algorithm with its improvement techniques. Experimental evaluation demonstrates efficient recognition performance of the proposed system compared with an active 3D face recognition system and a passive 3D face recognition system employing the original ICP algorithm.

This paper presents a palmprint recognition algorithm using Phase-Only Correlation (POC). The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes it possible to achieve highly robust image registration and matching. In the proposed algorithm, POC is used to align scaling, rotation and translation between two palmprint images, and evaluate similarity between them. Experimental evaluation using a palmprint image database clearly demonstrates efficient matching performance of the proposed algorithm.

In mass disasters such as earthquakes, fire disasters, tsunami, and terrorism, dental records have been used for identifying victims due to their processing time and accuracy. The greater the number of victims, the more time the identification tasks require, since a manual comparison between the dental radiograph records is done by forensic experts. Addressing this problem, this paper presents an efficient dental radiograph recognition system using Phase-Only Correlation (POC) for human identification. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of dental radiograph images makes possible to achieve highly robust image registration and recognition. Experimental evaluation using a set of dental radiographs indicates that the proposed system exhibits efficient recognition performance for low-quality images.

This paper presents an enhanced side-channel attack using a phase-based waveform matching technique. Conventionally, side-channel attacks such as Simple Power Analysis (SPA) and Differential Power Analysis (DPA) capture signal waveforms (e.g., power traces) with a trigger signal or a system clock, and use a statistical analysis of the waveforms to reduce noise and to retrieve secret information. However, the waveform data often includes displacement errors, and this degrades the accuracy of the statistical analysis. The use of a Phase-Only Correlation (POC) technique makes it possible to estimate the displacements between the signal waveforms with higher resolution than the sampling resolution. The accuracy of side-channel attacks can be enhanced using the POC-based matching method. Also, a popular DPA countermeasure of creating distorted waveforms with random delays can be defeated by our method. In this paper, we demonstrate the advantages of the proposed method in comparison with conventional approaches of experimental DPA and Differential ElectroMagnetic Analysis (DEMA) against DES software and hardware implementations.

This paper presents a high-accuracy 3D (three-dimen-sional) measurement system using multi-camera passive stereo vision to reconstruct 3D surfaces of free form objects. The proposed system is based on an efficient stereo correspondence technique, which consists of (i) coarse-to-fine correspondence search, and (ii) outlier detection and correction, both employing phase-based image matching. The proposed sub-pixel correspondence search technique contributes to dense reconstruction of arbitrary-shaped 3D surfaces with high accuracy. The outlier detection and correction technique contributes to high reliability of reconstructed 3D points. Through a set of experiments, we show that the proposed system measures 3D surfaces of objects with sub-mm accuracy. Also, we demonstrate high-quality dense 3D reconstruction of a human face as a typical example of free form objects. The result suggests a potential possibility of our approach to be used in many computer vision applications.

This paper presents a technique for high-accuracy correspondence search between two images using Phase-Only Correlation (POC) and its performance evaluation in a 3D measurement application. The proposed technique employs (i) a coarse-to-fine strategy using image pyramids for correspondence search and (ii) a sub-pixel window alignment technique for finding a pair of corresponding points with sub-pixel displacement accuracy. Experimental evaluation shows that the proposed method makes possible to estimate the displacement between corresponding points with approximately 0.05-pixel accuracy when using 1111-pixel matching windows. This paper also describes an application of the proposed technique to passive 3D measurement system.