Electronics

Cross-modality face recognition is an emerging topic due to the widespread usage of different sensors in day-today life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer's face. An Institutional Research Board protocol was obtained and images were collected from students, staff, faculty, and their family members at Tufts University. The database includes over 10,000 images from 113 individuals from more than 15 different countries, various gender identities, ages, and ethnic backgrounds. The contributions of this work are: 1) Detailed description of the content and acquisition procedure for images in the Tufts Face Database; 2) The Tufts Face Database is publicly available to researchers worldwide, which will allow assessment and creation of more robust, consistent, and adaptable recognition algorithms; 3) A comprehensive, up-to-date review on face recognition systems and face datasets.

Cross-modality face recognition is an emerging topic due to the widespread usage of different sensors in day-today life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer's face. An Institutional Research Board protocol was obtained and images were collected from students, staff, faculty, and their family members at Tufts University. The database includes over 10,000 images from 113 individuals from more than 15 different countries, various gender identities, ages, and ethnic backgrounds. The contributions of this work are: 1) Detailed description of the content and acquisition procedure for images in the Tufts Face Database; 2) The Tufts Face Database is publicly available to researchers worldwide, which will allow assessment and creation of more robust, consistent, and adaptable recognition algorithms; 3) A comprehensive, up-to-date review on face recognition systems and face datasets.

Post-mortem fingerprints are a valuable biometric used to aid in the identification of a deceased individual. However, fingerprints from the deceased undergo decomposition leading to indefinite structure when compared to ante-mortem fingerprints. Moreover, the performance of the existing two-dimensional (2D) fingerprint recognition systems is still below the expected potential. These problems arise because fingerprints are generally captured by manipulating a finger against a plane. In post-mortem fingerprint recovery, the decedent's finger must go through several reconditioning processes to prevent the rapid onslaught of decomposition. To address these deficiencies associated with the 2D systems, three-dimensional (3D) scanning systems have been employed to capture fingerprints. 3D technology is still in its transient phase, and is limited primarily by (i) the lack of existing 3D databases, (ii) the deficiency of 3D to 2D fingerprint image mapping algorithms, (iii) the incapacity to model and recreate the 2D fingerprint capturing procedure to improve 3D-2D fingerprint verification, and (iv) the inability to apply traditional fingerprint unrolling techniques on post-mortem 3D fingerprints. This paper presents a novel method to perform postmortem 3D fingerprint unrolling and pressure simulation to produce fingerprint images that are compatible with 2D fingerprint recognition systems. The thrust of this work strives to (i) develop a correspondence between 3D touchless and contact-based 2D fingerprint images, (ii) model fingerprints with deformities to provide a viable fingerprint image for matching, and (iii) develop a Mosaic Pressure Simulation (MPS) algorithm to recreate the effects of 2D fingerprint capturing procedure.

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 (a) establishing a metric to evaluate the image quality accurately, and (b) utilizing this metric to enable an automated enhancement process. This article 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 poorquality 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 4 different sensors and a total of 240 images (80 from each sensor) is used to evaluate the performance of the presented algorithms and methods. Computer simulations demonstrate that the LQM measure is useful in predicting the quality of the fingerprint images captured from various devices. Furthermore, experiments show that LQME can recover retrievable-corrupt fingerprint regions.

Thermal imaging is a process of using infrared radiation and thermal energy to collect information about objects. It is superior to visible imaging for its ability to operate in darkness and tolerate illumination variations. In addition, it has potential to penetrate smoke, aerosol, dust, and mist, which are critical inhibitors for visible imaging applications, including semantic segmentation. Unfortunately, current state-of-the-art image semantic segmentation methods (i) mainly concentrate on visible spectrum images, which do not adequately capture the context of corresponding pixels, particularly edge details in thermal images, and (ii) accept a trade-off between higher accuracy and lower speed, or vice-versa. Here, a novel end-to-end trainable convolutional neural network architecture, feature transverse network (FTNet), has been proposed to solve the aforementioned problems. FTNet captures and optimizes feature representation at the multi-scale resolution, thereby improving the capability to process high-resolution images and producing quality output with a lower computational cost. Extensive computer experimentations were conducted on publicly available benchmarking thermal datasets, including SODA, MFNet, and SCUT-Seg, to demonstrate the effectiveness of the proposed FTNet compared to state-of-the-art methods. This comparison includes multiple aspects, including the quantitative accuracy and speed of the various approaches.