Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries

Left Anterior Temporal Lobe and Bilateral Anterior Cingulate Cortex Are Semantic Hub Regions: Evidence from Behavior-Nodal Degree Mapping in Brain-Damaged Patients

The organizational principles of semantic memory in the human brain are still controversial. Although studies have shown that the semantic system contains hub regions that bind information from different sensorimotoric modalities to form concepts, it is unknown whether there are hub regions other than the anterior temporal lobe (ATL). Meanwhile, previous studies have rarely used network measurements to explore the hubs or correlated network indexes with semantic performance, although the most direct supportive evidence of hubs should come from the network perspective. To fill this gap, we correlated the brain-network index with semantic performance in 86 brain-damaged patients. We especially selected the nodal degree measure that reflects how well a node is connected in the network. The measure was calculated as the total number of connections of a given node with other nodes in the resting-state functional MRI network. Semantic ability was measured using the performance of both general and modality-specific (object form, color, motion, sound, manipulation, and function) semantic tasks. We found that the left ATL and the bilateral anterior cingulate cortex could be semantic hubs because the reduced nodal degree values of these regions could effectively predict the deficits in both general and modality-specific semantic performance. Moreover, the effects remained when the analyses were performed only in the patients who did not have lesions in these regions. The two hub regions might support semantic representations and executive control processes, respectively. These data provide empirical evidence for the distributed-plus-hub theory of semantic memory from the network perspective.</p

Recognizing Human Faces: Physical Modeling and Pattern Classification

Although significant work has been done in the field of face recognition, the performance of the state-of-the-art face recognition algorithms is not good enough to be effective in operational systems. Most algorithms work well for controlled images but are quite susceptible to changes in illumination, pose, etc. In this dissertation, we propose methods which address these issues, to recognize faces in more realistic scenarios. The developed approaches show the importance of physical modeling, contextual constraints and pattern classification for this task. For still image-based face recognition, we develop an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources, given just a single image. Though the problem is ill-posed in its generality, linear approximations to the subspace of Lambertian images in combination with rank constraints on unknown facial shape and albedo are used to make it tractable. In addition, we develop a purely geometric illumination-invariant matching algorithm that makes use of the bilateral symmetry of human faces. In particular, we prove that the set of images of bilaterally symmetric objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. For recognizing faces in videos, the challenge lies in suitable characterization of faces using the information available in the video. We propose a method that models a face as a linear dynamical system whose appearance changes with pose. Though the proposed method performs very well on the available datasets, it does not explicitly take the 3D structure or illumination conditions into account. To address these issues, we propose an algorithm to perform 3D facial pose tracking in videos. The approach combines the structural advantages of geometric modeling with the statistical advantages of a particle filter based inference to recover the 3D configuration of facial features in each frame of the video. The recovered 3D configuration parameters are further used to recognize faces in videos. From a pattern classification point of view, automatic face recognition presents a very unique challenge due to the presence of just one (or a few) sample(s) per identity. To address this, we develop a cohort-based framework that makes use of the large number of non-match samples present in the database to improve verification and identification performance

A Multimodal and Multi-Algorithmic Architecture for Data Fusion in Biometric Systems

Software di autenticazione basato su tratti biometric

Gait pattern analysis in the home environment as a key factor for the reliable assessment of shunt responsiveness in patients with idiopathic normal pressure hydrocephalus

BACKGROUND: The identification of patients with gait disturbance associated with idiopathic normal pressure hydrocephalus (iNPH) is challenging. This is due to the multifactorial causes of gait disturbance in elderly people and the single moment examination of laboratory tests. OBJECTIVE: We aimed to assess whether the use of gait sensors in a patient's home environment could help establish a reliable diagnostic tool to identify patients with iNPH by differentiating them from elderly healthy controls (EHC). METHODS: Five wearable inertial measurement units were used in 11 patients with iNPH and 20 matched EHCs. Data were collected in the home environment for 72 h. Fifteen spatio-temporal gait parameters were analyzed. Patients were examined preoperatively and postoperatively. We performed an iNPH sub-group analysis to assess differences between responders vs. non-responders. We aimed to identify parameters that are able to predict a reliable response to VP-shunt placement. RESULTS: Nine gait parameters significantly differ between EHC and patients with iNPH preoperatively. Postoperatively, patients with iNPH showed an improvement in the swing phase (p = 0.042), and compared to the EHC group, there was no significant difference regarding the cadence and traveled arm distance. Patients with a good VP-shunt response (NPH recovery rate of ≥5) significantly differ from the non-responders regarding cycle time, cycle time deviation, number of steps, gait velocity, straight length, stance phase, and stance to swing ratio. A receiver operating characteristic analysis showed good sensitivity for a preoperative stride length of ≥0.44 m and gait velocity of ≥0.39 m/s. CONCLUSION: There was a significant difference in 60% of the analyzed gait parameters between EHC and patients with iNPH, with a clear improvement toward the normalization of the cadence and traveled arm distance postoperatively, and a clear improvement of the swing phase. Patients with iNPH with a good response to VP-shunt significantly differ from the non-responders with an ameliorated gait pattern

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Essays on Children's Skill Formation

abstract: The dissertation is composed by three chapters. In Chapter 2 (coauthored with Matthew Wiswall) I develop new results for the identification and estimation of the technology of children’s skill formation when children’s skills are unobserved. In Chapter 3 I shed light on the importance of dynamic equilibrium interdependencies between children’s social interactions and parental investments decisions in explaining developmental differences between different social environments. In Chapter 4 (coauthored with Giuseppe Sorrenti) I study the effect of family income and maternal hours worked on both cognitive and behavioral child development.Dissertation/ThesisDoctoral Dissertation Economics 201