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- All Subjects: Computer Science
- Creators: Yang, Yezhou
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper presents an overview of The Dyadic Interaction Assistant for Individuals with Visual Impairments with a focus on the software component. The system is designed to communicate facial information (facial Action Units, facial expressions, and facial features) to an individual with visual impairments in a dyadic interaction between two people sitting across from each other. Comprised of (1) a webcam, (2) software, and (3) a haptic device, the system can also be described as a series of input, processing, and output stages, respectively. The processing stage of the system builds on the open source FaceTracker software and the application Computer Expression Recognition Toolbox (CERT). While these two sources provide the facial data, the program developed through the IDE Qt Creator and several AppleScripts are used to adapt the information to a Graphical User Interface (GUI) and output the data to a comma-separated values (CSV) file. It is the first software to convey all 3 types of facial information at once in real-time. Future work includes testing and evaluating the quality of the software with human subjects (both sighted and blind/low vision), integrating the haptic device to complete the system, and evaluating the entire system with human subjects (sighted and blind/low vision).
ContributorsBrzezinski, Chelsea Victoria (Author) / Balasubramanian, Vineeth (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification neural networks to observe which combinations applied to this framework were most effective in maximizing classification accuracy. Furthermore, the reasons why particular combinations were more effective than others is explored.
ContributorsMazboudi, Yassine Ahmad (Author) / Yang, Yezhou (Thesis director) / Ren, Yi (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder & Silverman, 1988). My study analyzes the traditional approach to learning coding skills which is unnatural to engineering students with no previous exposure and examining if visual learning enhances introductory computer science education. Visual and text-based learning are evaluated to determine how students learn introductory coding skills and associated problem solving skills. My study was conducted to observe how the two types of learning aid the students in learning how to problem solve as well as how much knowledge can be obtained in a short period of time. The application used for visual learning was Scratch and Repl.it was used for text-based learning. Two exams were made to measure the progress made by each student. The topics covered by the exam were initialization, variable reassignment, output, if statements, if else statements, nested if statements, logical operators, arrays/lists, while loop, type casting, functions, object orientation, and sorting. Analysis of the data collected in the study allow us to observe whether the traditional method of teaching programming or block-based programming is more beneficial and in what topics of introductory computer science concepts.
ContributorsVidaure, Destiny Vanessa (Author) / Meuth, Ryan (Thesis director) / Yang, Yezhou (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Facial Expressions Recognition using the Convolution Neural Network has been actively researched upon in the last decade due to its high number of applications in the human-computer interaction domain. As Convolution Neural Networks have the exceptional ability to learn, they outperform the methods using handcrafted features. Though the state-of-the-art models achieve high accuracy on the lab-controlled images, they still struggle for the wild expressions. Wild expressions are captured in a real-world setting and have natural expressions. Wild databases have many challenges such as occlusion, variations in lighting conditions and head poses. In this work, I address these challenges and propose a new model containing a Hybrid Convolutional Neural Network with a Fusion Layer. The Fusion Layer utilizes a combination of the knowledge obtained from two different domains for enhanced feature extraction from the in-the-wild images. I tested my network on two publicly available in-the-wild datasets namely RAF-DB and AffectNet. Next, I tested my trained model on CK+ dataset for the cross-database evaluation study. I prove that my model achieves comparable results with state-of-the-art methods. I argue that it can perform well on such datasets because it learns the features from two different domains rather than a single domain. Last, I present a real-time facial expression recognition system as a part of this work where the images are captured in real-time using laptop camera and passed to the model for obtaining a facial expression label for it. It indicates that the proposed model has low processing time and can produce output almost instantly.
ContributorsChhabra, Sachin (Author) / Li, Baoxin (Thesis advisor) / Venkateswara, Hemanth (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
There has been an explosion in the amount of data on the internet because of modern technology – especially image data – as a consequence of an exponential growth in the number of cameras existing in the world right now; from more extensive surveillance camera systems to billions of people walking around with smartphones in their pockets that come with built-in cameras. With this sudden increase in the accessibility of cameras, most of the data that is getting captured through these devices is ending up on the internet. Researchers soon took leverage of this data by creating large-scale datasets. However, generating a dataset – let alone a large-scale one – requires a lot of man-hours. This work presents an algorithm that makes use of optical flow and feature matching, along with utilizing localization outputs from a Mask R-CNN, to generate large-scale vehicle datasets without much human supervision. Additionally, this work proposes a novel multi-view vehicle dataset (MVVdb) of 500 vehicles which is also generated using the aforementioned algorithm.There are various research problems in computer vision that can leverage a multi-view dataset, e.g., 3D pose estimation, and 3D object detection. On the other hand, a multi-view vehicle dataset can be used for a 2D image to 3D shape prediction, generation of 3D vehicle models, and even a more robust vehicle make and model recognition. In this work, a ResNet is trained on the multi-view vehicle dataset to perform vehicle re-identification, which is fundamentally similar to a vehicle make and recognition problem – also showcasing the usability of the MVVdb dataset.
ContributorsGuha, Anubhab (Author) / Yang, Yezhou (Thesis advisor) / Lu, Duo (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2022
Description
In recent years, there has been significant progress in deep learning and computer vision, with many models proposed that have achieved state-of-art results on various image recognition tasks. However, to explore the full potential of the advances in this field, there is an urgent need to push the processing of deep networks from the cloud to edge devices. Unfortunately, many deep learning models cannot be efficiently implemented on edge devices as these devices are severely resource-constrained. In this thesis, I present QU-Net, a lightweight binary segmentation model based on the U-Net architecture. Traditionally, neural networks consider the entire image to be significant. However, in real-world scenarios, many regions in an image do not contain any objects of significance. These regions can be removed from the original input allowing a network to focus on the relevant regions and thus reduce computational costs. QU-Net proposes the salient regions (binary mask) that the deeper models can use as the input. Experiments show that QU-Net helped achieve a computational reduction of 25% on the Microsoft Common Objects in Context (MS COCO) dataset and 57% on the Cityscapes dataset. Moreover, QU-Net is a generalizable model that outperforms other similar works, such as Dynamic Convolutions.
ContributorsSanthosh Kumar Varma, Rahul (Author) / Yang, Yezhou (Thesis advisor) / Fan, Deliang (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. This thesis introduces a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. This system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this a novel sparse adversarial model, Sparse ReguLarized Generative Adversarial Network (SRLGAN), is developed for Cold-Start Recommendation. SRLGAN leverages the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. The performance of SRLGAN is evaluated on two popular datasets and demonstrate state-of-the-art results.
ContributorsShah, Aksheshkumar Ajaykumar (Author) / Venkateswara, Hemanth (Thesis advisor) / Berman, Spring (Thesis advisor) / Ladani, Leila J (Committee member) / Arizona State University (Publisher)
Created2022