2,084 research outputs found
3d Face Reconstruction And Emotion Analytics With Part-Based Morphable Models
3D face reconstruction and facial expression analytics using 3D facial data are new
and hot research topics in computer graphics and computer vision. In this proposal, we first
review the background knowledge for emotion analytics using 3D morphable face model, including
geometry feature-based methods, statistic model-based methods and more advanced
deep learning-bade methods. Then, we introduce a novel 3D face modeling and reconstruction
solution that robustly and accurately acquires 3D face models from a couple of images
captured by a single smartphone camera. Two selfie photos of a subject taken from the
front and side are used to guide our Non-Negative Matrix Factorization (NMF) induced
part-based face model to iteratively reconstruct an initial 3D face of the subject. Then, an
iterative detail updating method is applied to the initial generated 3D face to reconstruct
facial details through optimizing lighting parameters and local depths. Our iterative 3D
face reconstruction method permits fully automatic registration of a part-based face representation
to the acquired face data and the detailed 2D/3D features to build a high-quality
3D face model. The NMF part-based face representation learned from a 3D face database
facilitates effective global and adaptive local detail data fitting alternatively. Our system
is flexible and it allows users to conduct the capture in any uncontrolled environment. We
demonstrate the capability of our method by allowing users to capture and reconstruct their
3D faces by themselves.
Based on the 3D face model reconstruction, we can analyze the facial expression and
the related emotion in 3D space. We present a novel approach to analyze the facial expressions
from images and a quantitative information visualization scheme for exploring this
type of visual data. From the reconstructed result using NMF part-based morphable 3D face
model, basis parameters and a displacement map are extracted as features for facial emotion
analysis and visualization. Based upon the features, two Support Vector Regressions (SVRs)
are trained to determine the fuzzy Valence-Arousal (VA) values to quantify the emotions.
The continuously changing emotion status can be intuitively analyzed by visualizing the
VA values in VA-space. Our emotion analysis and visualization system, based on 3D NMF
morphable face model, detects expressions robustly from various head poses, face sizes and
lighting conditions, and is fully automatic to compute the VA values from images or a sequence
of video with various facial expressions. To evaluate our novel method, we test our
system on publicly available databases and evaluate the emotion analysis and visualization
results. We also apply our method to quantifying emotion changes during motivational interviews.
These experiments and applications demonstrate effectiveness and accuracy of
our method.
In order to improve the expression recognition accuracy, we present a facial expression
recognition approach with 3D Mesh Convolutional Neural Network (3DMCNN) and a visual
analytics guided 3DMCNN design and optimization scheme. The geometric properties of the
surface is computed using the 3D face model of a subject with facial expressions. Instead of
using regular Convolutional Neural Network (CNN) to learn intensities of the facial images,
we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We
design a geodesic distance-based convolution method to overcome the difficulties raised from
the irregular sampling of the face surface mesh. We further present an interactive visual
analytics for the purpose of designing and modifying the networks to analyze the learned
features and cluster similar nodes in 3DMCNN. By removing low activity nodes in the network,
the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and
analyze the effectiveness of our method by studying representative cases. Testing on public
datasets, our method achieves a higher recognition accuracy than traditional image-based
CNN and other 3D CNNs. The presented framework, including 3DMCNN and interactive
visual analytics of the CNN, can be extended to other applications
DBC based Face Recognition using DWT
The applications using face biometric has proved its reliability in last
decade. In this paper, we propose DBC based Face Recognition using DWT (DBC-
FR) model. The Poly-U Near Infra Red (NIR) database images are scanned and
cropped to get only the face part in pre-processing. The face part is resized
to 100*100 and DWT is applied to derive LL, LH, HL and HH subbands. The LL
subband of size 50*50 is converted into 100 cells with 5*5 dimention of each
cell. The Directional Binary Code (DBC) is applied on each 5*5 cell to derive
100 features. The Euclidian distance measure is used to compare the features of
test image and database images. The proposed algorithm render better percentage
recognition rate compared to the existing algorithm.Comment: 15 pages,9 figures, 4 table
3D Human Face Reconstruction and 2D Appearance Synthesis
3D human face reconstruction has been an extensive research for decades due to its wide applications, such as animation, recognition and 3D-driven appearance synthesis. Although commodity depth sensors are widely available in recent years, image based face reconstruction are significantly valuable as images are much easier to access and store.
In this dissertation, we first propose three image-based face reconstruction approaches according to different assumption of inputs.
In the first approach, face geometry is extracted from multiple key frames of a video sequence with different head poses. The camera should be calibrated under this assumption.
As the first approach is limited to videos, we propose the second approach then focus on single image. This approach also improves the geometry by adding fine grains using shading cue. We proposed a novel albedo estimation and linear optimization algorithm in this approach.
In the third approach, we further loose the constraint of the input image to arbitrary in the wild images. Our proposed approach can robustly reconstruct high quality model even with extreme expressions and large poses.
We then explore the applicability of our face reconstructions on four interesting applications: video face beautification, generating personalized facial blendshape from image sequences, face video stylizing and video face replacement. We demonstrate great potentials of our reconstruction approaches on these real-world applications. In particular, with the recent surge of interests in VR/AR, it is increasingly common to see people wearing head-mounted displays. However, the large occlusion on face is a big obstacle for people to communicate in a face-to-face manner. Our another application is that we explore hardware/software solutions for synthesizing the face image with presence of HMDs. We design two setups (experimental and mobile) which integrate two near IR cameras and one color camera to solve this problem. With our algorithm and prototype, we can achieve photo-realistic results.
We further propose a deep neutral network to solve the HMD removal problem considering it as a face inpainting problem. This approach doesn\u27t need special hardware and run in real-time with satisfying results
Comparison between Feature Based and Deep Learning Recognition Systems for Handwriting Arabic Numbers
Feature extraction from images is an essential part of the recognition system. Calculating the appropriate features is critical to the part of the classification process. However, there are no standard features nor a widely accepted feature set exist applied to all applications, features must be application dependent. In contrast, deep learning extract features from an image without need for human hard-coding the features extraction process. This can be very useful to build a model for classification which can classify any type of images after trained with enough images with labels then the trained model can be used in different recognition applications to classify. This paper presents two techniques to build recognition system for Arabic handwriting numbers, the feature-based method shows accepted results. However, the deep learning method gives more accurate results and required less study on how Arabic number is written and no hand-coding algorithms needed for feature extraction to be used in the classification process. Keywords: Handwriting Recognition, Image Processing, Features Extraction, Machine Learning, Deep Learning, Classification
Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform
In this research, off-line handwriting recognition system for Arabic alphabet is
introduced. The system contains three main stages: preprocessing, segmentation and
recognition stage. In the preprocessing stage, Radon transform was used in the design
of algorithms for page, line and word skew correction as well as for word slant
correction. In the segmentation stage, Hough transform approach was used for line
extraction. For line to words and word to characters segmentation, a statistical method
using mathematic representation of the lines and words binary image was used.
Unlike most of current handwriting recognition system, our system simulates the
human mechanism for image recognition, where images are encoded and saved in
memory as groups according to their similarity to each other. Characters are
decomposed into a coefficient vectors, using fast wavelet transform, then, vectors,
that represent a character in different possible shapes, are saved as groups with one
representative for each group. The recognition is achieved by comparing a vector of
the character to be recognized with group representatives.
Experiments showed that the proposed system is able to achieve the recognition task
with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a
single character in a text of 15 lines where each line has 10 words on average
Analysis of Signal Decomposition and Stain Separation methods for biomedical applications
Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis
Deep Face Morph Detection Based on Wavelet Decomposition
Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this dissertation, we delve into data-driven morph detection which is of great significance in terms of national security.
We propose several wavelet-based face morph detection schemes which employ some of the computer vision algorithms such as image wavelet analysis, group sparsity, feature selection, and the visual attention mechanisms. Wavelet decomposition enables us to leverage the fine-grained frequency content of an image to boost localizing manipulated areas in an image. Our methodologies are as follows: (1) entropy-based single morph detection, (2) entropy-based differential morph detection, (3) morph detection using group sparsity, and (4) Attention aware morph detection. In the first methodology, we harness mismatches between the entropy distribution of wavelet subbands corresponding to a pair of real and morph images to find a subset of most discriminative wavelet subbands which leads to an increase of morph detection accuracy. As the second methodology, we adopt entropy-based subband selection to tackle differential morph detection. In the third methodology, group sparsity is leveraged for subband selection. In other words, adding a group sparsity constraint to the loss function of our DNN leads to an implicit subband selection. Our fourth methodology consists of different types of visual attention mechanisms such as convolutional block attention modules and self-attention resulting in boosting morph detection accuracy.
We demonstrate efficiency of our proposed algorithms through several morph datasets via extensive evaluations as well as visualization methodologies
- …