Reliable evaluation of damage in vehicles is a primary concern in the insurance industry.Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm.During the assessment, precise damage segmentation plays a crucial role.Dent is a type of damage that can commonly occur in vehicles.It is difficult to pinpoint and tends to blend in with the background.This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims.Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting.The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations.CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks.Simultaneously, the weight mask is incorporated into DICE loss.Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss.Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation.

Automatically detecting conversational groups from video footage is an important research area with applications in social robotics, human-computer interaction, and video analysis. In this paper, we use a novel approach based on Graph Convolutional Networks (GCN) for the social group detection problem. We base our approach on a method from the community detection domain called Deep Modularity Networks (DMoN). As an input to the DMoN method, we construct a graph representation using body view frustums, which indicates affinities among the individuals. In addition to the frame-level group detector, the proposed system utilizes temporal information to improve the group detection accuracy. Experiments conducted on the standard benchmark dataset SALSA confirm that our GCN-based approach improves the group detection quality over state-of-the-art group detection methods.

Face is a highly utilized biometric, and 3D modality is preferred due to better handling of variations such as pose and illumination. However, occlusions covering the face alter the 3D surface and degrade the recognition performance. To improve recognition rates, the occluded parts should be detected prior to any surface comparison. In this paper, we consider two different occlusion detection approaches: The first one is based on statistical facial surface modeling, where pixel-wise Gaussian Mixture Models are trained. The second algorithm considers occlusion detection as a binary image segmentation problem: The regional cues of depth values are incorporated with neighborhood cues, and the acquired surface is modeled as a graph. The surface pixels are labeled as either face or occlusion via the graph cut technique. Experiments on the Bosphorus and the UMB-DB databases, including realistic occlusion variations, show that both methods improve occlusion detection and face recognition rates as compared to the baseline technique.

With advances in sensor technology, the three-dimensional (3-D) face has become an emerging biometric modality, preferred especially in high security applications. However, dealing with occlusions covering the facial surface is a great challenge, which should be handled to enable applicability to fully automatic security systems. In this paper, we propose a fully automatic 3-D face recognition system which is robust to occlusions. We basically consider two problems: 1) occlusion handling for surface registration, and 2) missing data handling for classification based on subspace analysis techniques. For the alignment problem, we employ an adaptively-selected-model-based registration scheme, where a face model is selected for an occluded face such that only the valid nonoccluded patches are utilized. After registering to the model, occlusions are detected and removed. In the classification stage, a masking strategy, which we call masked projection, is proposed to enable the use of subspace analysis techniques with incomplete data. Furthermore, a regional scheme suitable for occlusion handling is incorporated in classification to improve the overall results. Experimental results on two databases with realistic facial occlusions, namely, the Bosphorus and the UMB-DB, are reported. Experimental results confirm that registration based on the adaptively selected model together with the masked subspace analysis classification offer an occlusion robust face recognition system.

Biometric identification from three-dimensional (3-D) facial surface characteristics has become popular, especially in high security applications. In this paper, we propose a fully automatic expression insensitive 3-D face recognition system. Surface deformations due to facial expressions are a major problem in 3-D face recognition. The proposed approach deals with such challenging conditions in several aspects. First, we employ a fast and accurate region-based registration scheme that uses common region models. These common models make it possible to establish correspondence to all the gallery samples in a single registration pass. Second, we utilize curvature-based 3-D shape descriptors. Last, we apply statistical feature extraction methods. Since all the 3-D facial features are regionally registered to the same generic facial component, subspace construction techniques may be employed. We show that linear discriminant analysis significantly boosts the identification accuracy. We demonstrate the recognition ability of our system using the multiexpression Bosphorus and the most commonly used 3-D face database, Face Recognition Grand Challenge (FRGCv2). Our experimental results show that in both databases we obtain comparable performance to the best rank-1 correct classification rates reported in the literature so far: 98.19% for the Bosphorus and 97.51% for the FRGCv2 database. We have also carried out the standard receiver operating characteristics (ROC III) experiment for the FRGCv2 database. At an FAR of 0.1%, the verification performance was 86.09%. This shows that model-based registration is beneficial in identification scenarios where speed-up is important, whereas for verification one-to-one registration can be more beneficial.

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points.

Deformations caused by facial expression variations complicate the task of 3D face registration which is vital for successful 3D face recognition systems. In this work, we propose to use a hierarchical component-based face registration technique capable of handling the difficulties caused by non-rigid nature of faces. Local components independently registered by the iterative closest point (ICP) algorithm provides a fast registration with the use of a generic face model and does not suffer from non-rigidity of human facial surface. Invariance of the proposed approach is further increased by utilizing curvature-based 3D surface descriptors. Identification experiments conducted on the multi-expression Bosphorus database reveal that the accuracy of the classical ICP-based approach can be significantly increased under extreme expression variations.

In this paper, we present an extensive study of 3-D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used and the feature extraction algorithms that match best each representation type. We also consider novel applications of various feature extraction techniques such as discrete Fourier transform, discrete cosine transform, nonnegative matrix factorization, and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules voting- and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost fusion performance. In identification experiments performed on FRGC v1.0 and FRGC v2.0 face databases, we tried to find the answers to the following questions: 1) the relative importance of the face representation technique vis-à-vis the types of features extracted; 2) the impact of the gallery size; 3) the conditions, under which subspace methods are preferable, and the compression factor; 4) the most advantageous fusion level and fusion methods; 5) the role confidence votes in improving fusion and the style of selecting experts in the fusion; and 6) the consistency of the conclusions across different databases.

In local feature-based face recognition systems, the topographical locations of feature extractors directly affect the discriminative power of a recognizer. Better recognition accuracy can be achieved by the determination of the positions of salient image locations. Most of the facial feature selection algorithms in the literature work with two assumptions: one, that the importance of each feature is independent of the other features, and two, that the kernels should be located at fiducial points. Under these assumption, one can only get a sub-optimal solution. In this paper, we present a methodology that tries to overcome this problem by relaxing the two assumptions using a formalism of subset selection problem. We use a number of feature selection algorithms and a genetic algorithm. Comparative results on the FERET dataset confirm the viability of our approach.