Classification of red blood cell shapes in flow using outlier tolerant machine learning

The manual evaluation, classification and counting of biological objects demands for an enormous expenditure of time and subjective human input may be a source of error. Investigating the shape of red blood cells (RBCs) in microcapillary Poiseuille flow, we overcome this drawback by introducing a convolutional neural regression network for an automatic, outlier tolerant shape classification. From our experiments we expect two stable geometries: the so-called `slipper' and `croissant' shapes depending on the prevailing flow conditions and the cell-intrinsic parameters. Whereas croissants mostly occur at low shear rates, slippers evolve at higher flow velocities. With our method, we are able to find the transition point between both `phases' of stable shapes which is of high interest to ensuing theoretical studies and numerical simulations. Using statistically based thresholds, from our data, we obtain so-called phase diagrams which are compared to manual evaluations. Prospectively, our concept allows us to perform objective analyses of measurements for a variety of flow conditions and to receive comparable results. Moreover, the proposed procedure enables unbiased studies on the influence of drugs on flow properties of single RBCs and the resulting macroscopic change of the flow behavior of whole blood.Comment: 15 pages, published in PLoS Comput Biol, open acces

Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis

This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multiview gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness

On Detecting Faces And Classifying Facial Races With Partial Occlusions And Pose Variations

In this dissertation, we present our contributions in face detection and facial race classification. Face detection in unconstrained images is a traditional problem in computer vision community. Challenges still remain. In particular, the detection of partially occluded faces with pose variations has not been well addressed. In the first part of this dissertation, our contributions are three-fold. First, we introduce our four image datasets consisting of large-scale labeled face dataset, noisy large-scale labeled non-face dataset, CrowdFaces dataset, and CrowdNonFaces dataset intended to be used for face detection training. Second, we improve Viola-Jones (VJ) face detection results by first training a Convolutional Neural Network (CNN) model on our noisy datasets. We show our improvement over the VJ face detector on AFW face detection benchmark dataset. However, existing partial occluded face detection methods require training several models, computing hand-crafted features, or both. Hence, we thirdly propose our Large-Scale Deep Learning (LSDL), a method that does not require training several CNN models or hand-crafted features computations to detect faces. Our LSDL face detector is trained on a single CNN model to detect unconstrained multi-view partially occluded and non-partially occluded faces. The model is trained with a large number of face training examples that cover most partial occlusions and non-partial occlusions facial appearances. The LSDL face detection method is achieved by selecting detection windows with the highest confidence scores using a threshold. Our evaluation results show that our LSDL method achieves the best performance on AFW dataset and a comparable performance on FDDB dataset among state-of-the-art face detection methods without manually extending or adjusting the square detection bounding boxes. Many biometrics and security systems use facial information to obtain an individual identification and recognition. Classifying a race from a face image can provide a strong hint to search for facial identity and criminal identification. Current facial race classification methods are confined only to constrained non-partially occluded frontal faces. Challenges remain under unconstrained environments such as partial occlusions and pose variations, low illuminations, and small scales. In the second part of the dissertation, we propose a CNN model to classify facial races with partial occlusions and pose variations. The proposed model is trained using a broad and balanced racial distributed face image dataset. The model is trained on four major human races, Caucasian, Indian, Mongolian, and Negroid. Our model is evaluated against the state-of-the-art methods on a constrained face test dataset. Also, an evaluation of the proposed model and human performance is conducted and compared on our new unconstrained facial race benchmark (CIMN) dataset. Our results show that our model achieves 95.1% of race classification accuracy in the constrained environment. Furthermore, the model achieves a comparable accuracy of race classification compared to human performance on the current challenges in the unconstrained environment

Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition

Recognition of facial expressions is critical for successful social interactions and relationships. Facial expressions transmit emotional information, which is critical for human-machine interaction; therefore, significant research in computer vision has been conducted, with promising findings in using facial expression detection in both academia and industry. 3D pictures acquired enormous popularity owing to their ability to overcome some of the constraints inherent in 2D imagery, such as lighting and variation. We present a method for recognizing facial expressions in this article by combining features extracted from 2D textured pictures and 3D geometric data using the Local Binary Pattern (LBP) and the 3D Voxel Histogram of Oriented Gradients (3DVHOG), respectively. We performed various pre-processing operations using the MDPA-FACE3D and Bosphorus datasets, then we carried out classification process to classify images into seven universal emotions, namely anger, disgust, fear, happiness, sadness, neutral, and surprise. Using Support Vector Machine classifier, we achieved the accuracy of 88.5 % and 92.9 % on the MDPA-FACE3D and the Bosphorus datasets, respectively

Object Tracking Using Local Binary Descriptors

Visual tracking has become an increasingly important topic of research in the field of Computer Vision (CV). There are currently many tracking methods based on the Detect-then-Track paradigm. This type of approach may allow for a system to track a random object with just one initialization phase, but may often rely on constructing models to follow the object. Another limitation of these methods is that they are computationally and memory intensive, which hinders their application to resource constrained platforms such as mobile devices. Under these conditions, the implementation of Augmented Reality (AR) or complex multi-part systems is not possible. In this thesis, we explore a variety of interest point descriptors for generic object tracking. The SIFT descriptor is considered a benchmark and will be compared with binary descriptors such as BRIEF, ORB, BRISK, and FREAK. The accuracy of these descriptors is benchmarked against the ground truth of the object\u27s location. We use dictionaries of descriptors to track regions with small error under variations due to occlusions, illumination changes, scaling, and rotation. This is accomplished by using Dense-to-Sparse Search Pattern, Locality Constraints, and Scale Adaptation. A benchmarking system is created to test the descriptors\u27 accuracy, speed, robustness, and distinctness. This data offers a comparison of the tracking system to current state of the art systems such as Multiple Instance Learning Tracker (MILTrack), Tracker Learned Detection (TLD), and Continuously Adaptive MeanShift (CAMSHIFT)