Biblio

Emotion Detection through Facial feature recognition is an active domain of research in the field of human-computer interaction (HCI). Humans are able to share multiple emotions and feelings through their facial gestures and body language. In this project, in order to detect the live emotions from the human facial gesture, we will be using an algorithm that allows the computer to automatically detect the facial recognition of human emotions with the help of Convolution Neural Network (CNN) and OpenCV. Ultimately, Emotion Detection is an integration of obtained information from multiple patterns. If computers will be able to understand more of human emotions, then it will mutually reduce the gap between humans and computers. In this research paper, we will demonstrate an effective way to detect emotions like neutral, happy, sad, surprise, angry, fear, and disgust from the frontal facial expression of the human in front of the live webcam.

Facial emotion recognition helps feed the growth of the future artificial intelligence with the development of emotion recognition, learning, and analysis of different angles of a human face and head pose. The world's recent pandemic gave rise to the rapid installment of facial recognition for fewer applications, while emotion recognition is still within the experimental boundaries. The current challenges encountered with facial emotion recognition (FER) are the difference between background noises. Since today's world shows us that humans soon need robotics in the most significant role of human perception, attention, memory, decision-making, and human-robot interaction (HRI) needs employees. By merging the head pose as a combination towards the FER to boost the robustness in understanding emotions using the convolutional neural networks (CNN). The stochastic gradient descent with a comprehensive model is adopted by applying multi-task learning capable of implicit parallelism, inherent and better global optimizer in finding better network weights. After executing a multi-task learning model using two independent datasets, the experiment with the FER and head pose learning multi-views co-regularization frameworks were subsequently merged with validation accuracy.

Face recognition technology is widely employed in a variety of applications, including public security, criminal identification, multimedia data management, and so on. Because of its importance for practical applications and theoretical issues, the facial recognition system has received a lot of attention. Furthermore, numerous strategies have been offered, each of which has shown to be a significant benefit in the field of facial and pattern recognition systems. Face recognition still faces substantial hurdles in unrestricted situations, despite these advancements. Deep learning techniques for facial recognition are presented in this paper for accurate detection and identification of facial images. The primary goal of facial recognition is to recognize and validate facial features. The database consists of 500 color images of people that have been pre-processed and features extracted using Linear Discriminant Analysis. These features are split into 70 percent for training and 30 percent for testing of decision tree classifiers for the computation of face recognition system performance.

Facial is the most dynamic part of the human body that conveys information about emotions. The level of diversity in facial geometry and facial look makes it possible to detect various human expressions. To be able to differentiate among numerous facial expressions of emotion, it is crucial to identify the classes of facial expressions. The methodology used in this article is based on convolutional neural networks (CNN). In this paper Deep Learning CNN is used to examine Alex net architectures. Improvements were achieved by applying the transfer learning approach and modifying the fully connected layer with the Support Vector Machine(SVM) classifier. The system succeeded by achieving satisfactory results on icv-the MEFED dataset. Improved models achieved around 64.29 %of recognition rates for the classification of the selected expressions. The results obtained are acceptable and comparable to the relevant systems in the literature provide ideas a background for further improvements.

Facial expression intensity, which quantifies the degree of facial expression, has been proposed. It is calculated based on how much facial feature values change compared to an expressionless face. The estimation has two aspects. One is to classify facial expressions, and the other is to estimate their intensity. However, it is difficult to do them at the same time. There- fore, in this work, the estimation of intensity and the classification of expression are separated. We suggest an explicit method and an implicit method. In the explicit one, a classifier determines which types of expression the inputs are, and each regressor determines its intensity. On the other hand, in the implicit one, we give zero values or non-zero values to regressors for each type of facial expression as ground truth, depending on whether or not an input image is the correct facial expression. We evaluated the two methods and, as a result, found that they are effective for facial expression recognition.

Artificial intelligence (AI) cooperates with multiple industries to improve the overall industry framework. Especially, human emotion recognition plays an indispensable role in supporting medical care, psychological counseling, crime prevention and detection, and crime investigation. The research on emotion recognition includes emotion-specific intonation patterns, literal expressions of emotions, and facial expressions. Recently, the deep learning model of facial emotion recognition aims to capture tiny changes in facial muscles to provide greater recognition accuracy. Hybrid models in facial expression recognition have been constantly proposed to improve the performance of deep learning models in these years. In this study, we proposed an ensemble learning algorithm for the accuracy of the facial emotion recognition model with three deep learning models: VGG16, InceptionResNetV2, and EfficientNetB0. To enhance the performance of these benchmark models, we applied transfer learning, fine-tuning, and data augmentation to implement the training and validation of the Facial Expression Recognition 2013 (FER-2013) Dataset. The developed algorithm finds the best-predicted value by prioritizing the InceptionResNetV2. The experimental results show that the proposed ensemble learning algorithm of priorities edges up 2.81% accuracy of the model identification. The future extension of this study ventures into the Internet of Things (IoT), medical care, and crime detection and prevention.

In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset.

The major mode of communication between hearing-impaired or mute people and others is sign language. Prior, most of the recognition systems for sign language had been set simply to recognize hand signs and convey them as text. However, the proposed model tries to provide speech to the mute. Firstly, hand gestures for sign language recognition and facial emotions are trained using CNN (Convolutional Neural Network) and then by training the emotion to speech model. Finally combining hand gestures and facial emotions to realize the emotion and speech.

Child facial expression recognition is a relatively less investigated area within affective computing. Children’s facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children’s facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.

We propose versatile hardware framework for ECC. The framework supports arithmetic operations over P-256, Ed25519 and Curve25519 curves, enabling easy implementation of various ECC algorithms. Framework finds its application area e.g. in FIDO2 attestation or in nowadays rapidly expanding field of hardware wallets. As the design is intended to be ASIC-ready, we designed it to be area efficient. Hardware units are reused for calculations in several finite fields, and some of them are superior to previously designed circuits in terms of time-area product. The framework implements several attack countermeasures. It enables implementation of certain countermeasures even in later stages of design. The design was validated on SoC FPGA.

Having high safety and effective computational property, the elliptic curve cryptosystem is very suitable for embedded mobile environment with resource constraints. Power attack is a powerful cipher attack method, it uses leaking information of cipher-chip in its operation process to attack chip cryptographic algorithms. In view of the situation that the power attack on the elliptic curve cryptosystem mainly concentrates on scalar multiplication operation an improved algorithm FWNAF based on RWNAF is proposed. This algorithm utilizes the fragments window technology further improves the utilization ratio of the storage resource and reduces the “jitter phenomenon” in system computing performance caused by the sharp change in system resources.

The concept of a connected vehicle refers to the linking of vehicles to each other and to other things. Today, developments in the Internet of Things (IoT) and 5G have made a significant contribution to connected vehicle technology. In addition to many positive contributions, connected vehicle technology also brings with it many security-related problems. In this study, a digital signature algorithm based on elliptic curve cryptography is proposed to verify the message and identity sent to the vehicles. In the proposed model, with the anonymous identification given to the vehicle by the central unit, the vehicle is prevented from being detected by other vehicles and third parties. Thus, even if the personal data produced in the vehicles is shared, it cannot be found which vehicle it belongs to.

Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.

To contribute to medical data security, we propose the application of a modified algorithm on elliptical curves (ECC), initially proposed for text encryption. We implement this algorithm by eliminating the sender-receiver lookup table and grouping the pixel values into pairs to form points on a predefined elliptical curve. Simulation results show that the proposed algorithm offers the best compromise between the quality and the speed of cipher / decipher, especially for large images. A comparative study between ECC and AlGamel showed that the proposed algorithm offers better performance and its application, on medical images, is promising. Medical images contain many pieces of information and are often large. If the cryptographic operation is performed on every single pixel it will take more time. So, working on groups of pixels will be strongly recommended to save time and space.

Aim: Data is secured in the cloud using Elliptic Curve Cryptography (ECC) compared with Advanced Encryption Standard (AES) with improved time efficiency. Materials and Methods: Encryption and decryption time is performed with files stored in the cloud. Protecting data with improved time efficiency is carried out using ECC where the number of samples (\textbackslashmathrmN=6) and AES (\textbackslashmathrmN=6), obtained using the G-power value of 80%. Results: Mean time of ECC is 0.1683 and RSA is 0.7517. Significant value for the proposed system is 0.643 (\textbackslashmathrmp \textgreater 0.05). Conclusion: Within the limit of study, ECC performs faster in less consumption time when compared to AES.

Public key cryptography plays an important role in secure communications over insecure channels. Elliptic curve cryptography, as a variant of public key cryptography, has been extensively used in the last decades for such purposes. In this paper, we present a software tool for parallel generation of cryptographic keys based on elliptic curves. Binary method for point multiplication and C++ threads were used in parallel implementation, while secp256k1 elliptic curve was used for testing. Obtained results show speedup of 30% over the sequential solution for 8 threads. The results are briefly discussed in the paper.

Nowadays, cloud computing has become one of the most important and easily available storage options. This paper represents providing a platform where the data redundancy and the data security is maintained. Materials and Methods: This study contains two groups, the elliptic curve cryptography is developed in group 1 with 480 samples and advanced encryption is developed in group 2 with 960 samples. The accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Result: Advanced elliptic curve cryptography algorithm provides 1.2 times better performance compared to conventional elliptic curve cryptography algorithm for various datasets. The results were obtained with a significance value of 0.447 (p\textgreater0.05). Conclusion: From the obtained results the advanced elliptic curve cryptography algorithm seems to be better than the conventional algorithm.

The use of image data in multimedia communication based applications like military applications and medical images security applications are increasing every day and the secrecy of the image data is extremely important for such applications. A number of methods and techniques for securely transmitting images are proposed in the literature based on image encryption and steganography approaches. A novel mechanism for transmitting color images securely is proposed in this paper mainly based on public key cryptography mechanism also by combining the advantage of simplicity of symmetric schemes. The technique combines the strengths of Elliptic Curve Cryptography and the classical symmetric cryptographic mechanism called Hill Cipher encryption method. The technique also includes the concept of Magic Square for jumbling the pixels yielding maximum diffusion in the image pixels. In the performance evaluation, the proposed method proved that the new system works pretty well. The method is proved to be effective in maintaining the confidentiality of the image in transit and also for resisting security attacks.

This paper presents a novel approach for a facial security system using elliptic curve cryptography. Face images extracted from input video are encrypted before sending to a remote server. The input face images are completely encrypted by mapping each pixel value of the detected face from the input video frame to a point on an elliptic curve. The original image can be recovered when needed using the elliptic curve cryptography decryption function. Specifically, we modify point multiplication designed for projective coordinates and apply the modified approach in affine coordinates to speed up scalar point multiplication operation. Image encryption and decryption operations are also facilitated using our existing scheme. Simulation results on Visual Studio demonstrate that the proposed systems help accelerate encryption and decryption operations while maintaining information confidentiality.

Cloud Computing revolutionize the usage of Internet of Things enabled devices integrated via Internet. Providing everything in an outsourced fashion, Cloud also lends infrastructures such as storage. Though cloud makes it easy for us to store and access the data faster and easier, yet there exist various security and privacy risks. Such issues if not handled may become more threatening as it could even disclose the privacy of an individual/ organization. Strengthening the security of data is need of the hour. The work proposes a novel architecture enhancing the security of Cloud data in an IoT integrated environment. In order to enhance the security, systematic use of a modified hybrid mechanism based on DNA code and Elliptic Curve Cryptography along with Third Party Audit is proposed. The performance of the proposed mechanism has been analysed. The results ensures that proposed IoT Cloud architecture performs better while providing strong security which is the major aspect of the work.

Security authentication can effectively solve the problem of access to Low Earth Orbit (LEO) satellites. However, the existing solutions still harbor some problems in the computational complexity of satellite authentication, flexible networking, resistance to brute force attacks and other aspects. So, a security authentication scheme for LEO satellites that integrates spatial channel characteristics is designed within the software defined network architecture. In this scheme, the spatial channel characteristics are introduced to the subsequent lightweight encryption algorithm to achieve effective defense against brute force attacks. According to security analysis and simulation results, the scheme can effectively reduce the computational overhead while protecting against replay attacks, brute force attacks, DOS attacks, and other known attacks.

Cryptocurrencies such as Bitcoin and Ethereum achieve decentralized payment by maintaining a globally distributed and append-only ledger. Recently, several researchers have sought to achieve privacy-preserving auditing, which is a crucial function for scenarios that require regulatory compliance, for decentralized payment systems. However, those proposed schemes usually cost much time for the cooperation between the auditor and the user due to leveraging complex cryptographic tools such as zero-knowledge proof. To tackle the problem, we present T-PPA, a privacy-preserving decentralized payment system, which provides customizable and efficient auditability by leveraging trusted execution environments (TEEs). T-PPA demands the auditor construct audit programs based on request and execute them in the TEE to protect the privacy of transactions. Then, identity-based encryption (IBE) is employed to construct the separation of power between the agency nodes and the auditor and to protect the privacy of transactions out of TEE. The experimental results show that T-PPA can achieve privacy-preserving audits with acceptable overhead.

Data security is the process of protecting data from loss, alteration, or unauthorised access during its entire lifecycle. It includes everything from the policies and practices of a company to the hardware, software, storage, and user devices used by that company. Data security tools and technology increase transparency into an organization's data and its usage. These tools can protect data by employing methods including encryption and data masking personally identifiable information.. Additionally, the method aids businesses in streamlining their auditing operations and adhering to the increasingly strict data protection rules.

The development of cloud apps enables people to exchange resources, goods, and expertise online with other clients. The material is more vulnerable to numerous security dangers from outsiders due to the fact that millions of users exchange data through the same system. How to maintain the security of this data is now the main concern. The current data protection system functions best when it places a greater priority on safeguarding data maintained in online storage than it does on cybersecurity during transportation. The data becomes open to intrusion attacks while being transferred. Additionally, the present craze states that an outside auditor may view data as it is being transmitted. Additionally, by allowing the hacker to assume a third-person identity while obtaining the information, this makes the data more susceptible to exploitation. The proposed system focuses on using encryption to safeguard information flow since cybersecurity is seen as a major issue. The approach also takes into account the fourth auditing issue, which is that under the recommended manner, the inspector is not allowed to see the user information. Tests have shown that the recommended technique improves security overall by making it harder for hackers to decode the supplied data.

Research data sharing requires provision of adequate security. The requirements for data privacy are extremely demanding for medical data that is reused for research purposes. To address these requirements, the research institutions must implement adequate security measurements, and this demands large effort and costs to do it properly. The usage of adequate access controls and data encryption are key approaches to effectively protect research data confidentiality; however, the management of the encryption keys is challenging. There are novel mechanisms that can be explored for managing access to the encryption keys and encrypted files. These mechanisms guarantee that data are accessed by authorised users and that auditing is possible. In this paper we explore these mechanisms to implement a secure research medical data sharing system. In the proposed system, the research data are stored on a secure cloud system. The data are partitioned into subsets, each one encrypted with a unique key. After the authorisation process, researchers are given rights to use one or more of the keys and to selectively access and decrypt parts of the dataset. Our proposed solution offers automated fine-grain access control to research data, saving time and work usually made manually. Moreover, it maximises and fortifies users' trust in data sharing through secure clouds solutions. We present an initial evaluation and conclude with a discussion about the limitations, open research questions and future work around this challenging topic.