Model-based image analysis for forensic shoe print recognition

This thesis is about automated forensic shoe print recognition. Recognizing a shoe print in an image is an inherently difficult task. Shoe prints vary in their pose, shape and appearance. They are surrounded and partially occluded by other objects and may be left on a wide range of diverse surfaces. We propose to formulate this task in a model-based image analysis framework. Our framework is based on the Active Basis Model. A shoe print is represented as hierarchical composition of basis filters. The individual filters encode local information about the geometry and appearance of the shoe print pattern. The hierarchical com- position encodes mid- and long-range geometric properties of the object. A statistical distribution is imposed on the parameters of this representation, in order to account for the variation in a shoe print‘s geometry and appearance. Our work extends the Active Basis Model in various ways, in order to make it robustly applicable to the analysis of shoe print images. We propose an algorithm that automat- ically infers an efficient hierarchical dependency structure between the basis filters. The learned hierarchical dependencies are beneficial for our further extensions, while at the same time permitting an efficient optimization process. We introduce an occlusion model and propose to leverage the hierarchical dependencies to integrate contextual informa- tion efficiently into the reasoning process about occlusions. Finally, we study the effect of the basis filter on the discrimination of the object from the background. In this con- text, we highlight the role of the hierarchical model structure in terms of combining the locally ambiguous filter response into a sophisticated discriminator. The main contribution of this work is a model-based image analysis framework which represents a planar object‘s variation in shape and appearance, it‘s partial occlusion as well as background clutter. The model parameters are optimized jointly in an efficient optimization scheme. Our extensions to the Active Basis Model lead to an improved discriminative ability and permit coherent occlusions and hierarchical deformations. The experimental results demonstrate a new state of the art performance at the task of forensic shoe print recognition

COMPAS: Representation Learning with Compositional Part Sharing for Few-Shot Classification

Few-shot image classification consists of two consecutive learning processes: 1) In the meta-learning stage, the model acquires a knowledge base from a set of training classes. 2) During meta-testing, the acquired knowledge is used to recognize unseen classes from very few examples. Inspired by the compositional representation of objects in humans, we train a neural network architecture that explicitly represents objects as a set of parts and their spatial composition. In particular, during meta-learning, we train a knowledge base that consists of a dictionary of part representations and a dictionary of part activation maps that encode common spatial activation patterns of parts. The elements of both dictionaries are shared among the training classes. During meta-testing, the representation of unseen classes is learned using the part representations and the part activation maps from the knowledge base. Finally, an attention mechanism is used to strengthen those parts that are most important for each category. We demonstrate the value of our compositional learning framework for a few-shot classification using miniImageNet, tieredImageNet, CIFAR-FS, and FC100, where we achieve state-of-the-art performance

Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model

Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation as an out-of-task and out-of-distribution generalization problem. Specifically, we replace the fully connected classifier in neural networks with a Bayesian generative model of the neural network features. The model is trained from non-occluded images using bounding box annotations and class labels only, but is applied to generalize out-of-task to object segmentation and to generalize out-of-distribution to segment occluded objects. We demonstrate how such Bayesian models can naturally generalize beyond the training task labels when they learn a prior that models the object's background context and shape. Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries. Our algorithm outperforms alternative methods that use the same supervision by a large margin, and even outperforms methods where annotated amodal segmentations are used during training, when the amount of occlusion is large. Code is publically available at https://github.com/YihongSun/Bayesian-Amodal

Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.Comment: Accepted to CVPR 2018 Workshop on Analysis and Modeling of Faces and Gestures (AMFG

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to computer vision allow us to overcome this difficulty by explicitly modeling the physical image formation process. Using generative object models, the analysis of an observed image is performed via Bayesian inference of the posterior distribution. This conceptually simple approach tends to fail in practice because of several difficulties stemming from sampling the posterior distribution: high-dimensionality and multi-modality of the posterior distribution as well as expensive simulation of the rendering process. The main difficulty of sampling approaches in a computer vision context is choosing the proposal distribution accurately so that maxima of the posterior are explored early and the algorithm quickly converges to a valid image interpretation. In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution. Compared to a standard Gaussian random walk proposal, this accelerates the sampler in finding regions of the posterior with high value. In this way, we can significantly reduce the number of samples needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201

Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion

Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture Compositional Convolutional Neural Network. In particular, we propose to replace the fully connected classification head of a DCNN with a differentiable compositional model. The generative nature of the compositional model enables it to localize occluders and subsequently focus on the non-occluded parts of the object. We conduct classification experiments on artificially occluded images as well as real images of partially occluded objects from the MS-COCO dataset. The results show that DCNNs do not classify occluded objects robustly, even when trained with data that is strongly augmented with partial occlusions. Our proposed model outperforms standard DCNNs by a large margin at classifying partially occluded objects, even when it has not been exposed to occluded objects during training. Additional experiments demonstrate that CompositionalNets can also localize the occluders accurately, despite being trained with class labels only. The code used in this work is publicly available.Comment: CVPR 2020; Code is available https://github.com/AdamKortylewski/CompositionalNets; Supplementary material: https://adamkortylewski.com/data/compnet_supp.pd

General Neural Gauge Fields

The recent advance of neural fields, such as neural radiance fields, has significantly pushed the boundary of scene representation learning. Aiming to boost the computation efficiency and rendering quality of 3D scenes, a popular line of research maps the 3D coordinate system to another measuring system, e.g., 2D manifolds and hash tables, for modeling neural fields. The conversion of coordinate systems can be typically dubbed as gauge transformation, which is usually a pre-defined mapping function, e.g., orthogonal projection or spatial hash function. This begs a question: can we directly learn a desired gauge transformation along with the neural field in an end-to-end manner? In this work, we extend this problem to a general paradigm with a taxonomy of discrete & continuous cases, and develop an end-to-end learning framework to jointly optimize the gauge transformation and neural fields. To counter the problem that the learning of gauge transformations can collapse easily, we derive a general regularization mechanism from the principle of information conservation during the gauge transformation. To circumvent the high computation cost in gauge learning with regularization, we directly derive an information-invariant gauge transformation which allows to preserve scene information inherently and yield superior performance.Comment: ICLR 202