Interface: Technology & Portraiture

In Interface, five Kentucky-area artists explore a new language of representation with the aid of digital tools like Artificial Intelligence (AI) and Automatic Facial Recognition Software (AFR). Some artists use algorithms to alter celebrity faces beyond recognition, others feed data sets of existing art to AI models in an attempt to generate portraits of no one in particular. Others still create tools for understanding the very act of facial recognition or obfuscation. All have one thing in common: they wish to stretch the limits of and critique the genre of portraiture, as well as to cause viewers to question their assumptions about the genre’s scope and function.https://uknowledge.uky.edu/art_exhibitioncat_2023/1001/thumbnail.jp

Automatic facial recognition and the intensification of police surveillance

In R (on the application of Bridges) v Chief Constable of South Wales Police [2020] EWCA Civ 1058 the Court of Appeal held the deployment of live automated facial recognition technology (AFR) by the South Wales Police Force (SWP) unlawful on three grounds. It violated the right to respect for private life under Article 8 of the European Convention on Human Rights (ECHR) because it lacked a suitable basis in law; the Data Protection Impact Assessment carried out under section 64 of the Data Protection Act 2018 was deficient for failing to assess the risks to the rights and freedoms of individuals processed by the system; and SWP failed to fulfil the Public Service Equality Duty (PSED) imposed by section 149 of the Equality Act 2010 by failing to assess whether or not the software used in the AFR system was biased in relation to sex and race

Facing up to the challenge of behavioural observation in infant hearing assessment

The ability to assess detection and discrimination of speech by infants has proved elusive. Dr Iain Jackson and colleagues discuss how new technologies and fresh approaches might offer valuable insight into young infants’ behavioural responses to sound

Innovative Approach to Detect Mental Disorder Using Multimodal Technique

The human can display their emotions through facial expressions. To achieve more effective human- computer interaction, the emotion recognize from human face could prove to be an invaluable tool. In this work the automatic facial recognition system is described with the help of video. The main aim is to focus on detecting the human face from the video and classify the emotions on the basis of facial features .There have been extensive studies of human facial expressions. These facial expressions are representing happiness, sadness, anger, fear, surprise and disgust. It including preliterate ones, and found much commonality in the expression and recognition of emotions on the face. Emotion detection from speech has many important applications. In human-computer based systems, emotion recognition systems provide users with improved services as per their emotions criteria. It is quite limited on body of work on detecting emotion in speech. The developers are still debating what features effect the emotion identification in speech. There is no particularity for the best algorithm for classifying emotion, and which emotions to class together

Linking teachers’ facial microexpressions with student-based evaluation of teaching effectiveness: A pilot study using FaceReader™

[EN] This study seeks to investigate the potential influence of facial microexpressions on student-based evaluations and to explore the future possibilities of using automated technologies in higher education. We applied a non-experimental correlational design to investigate if the number of videotaped university lecturers’ facial microexpressions recognized by FaceReader™ serves as a predictor for positive results on student evaluation of teaching effectiveness. Therefore, we analyzed five videotaped lectures with the automatic facial recognition software. Additionally, each video was rated by between 8 and 16 students, using a rating instrument based on the results of Murray´s (1983) factor analysis. The FaceReader™ software could detect more than 5.000 facial microexpressions. Although positive emotions bear positive influence on the “overall performance rating”, “emotions” is not predicting “overall performance rating”, b = .05, t(37) = .35, p > .05. The study demonstrates that student ratings are affected by more variables than just facial microexpressions. The study showed that sympathy as well as the estimated age of the lecturer predicted higher student ratings.Sailer, M.; Schlag, R. (2021). Linking teachers’ facial microexpressions with student-based evaluation of teaching effectiveness: A pilot study using FaceReader™. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 555-562. https://doi.org/10.4995/HEAd21.2021.1309355556

Use of Facial Recognition Technique in Criminal Investigations in India

The social interactions of the modern period are characterised by the extensive use of technical tools for information processing. Considering the quick advancement of computer technology, they are already competent to perform challenging tasks needing the careful solution of technical and imaginative issues. The term "Artificial Intelligence" is successfully used in a wide range of activities, from smartphone screens to the creation of music and art. Legal academics are increasingly considering the need for using technical tools in criminal area in light of these circumstances, notably for deciding punishment and other kinds of criminal law influence against people who have committed destructive activities. Use of facial recognition technologies is rising in the post-COVID environment. Law enforcement organisations have seen significant gains in criminal investigation and crime prevention thanks to face recognition technologies, but there are also well-known privacy risks and data misuse issues. This essay explores the applications of Facial Recognition Technology in India and dissects the institutional and technological issues on its usage in law enforcement. Additionally, it offers both immediate and long-term answers that must be established before these technologies are widely used

Evaluation of face recognition algorithms under noise

One of the major applications of computer vision and image processing is face recognition, where a computerized algorithm automatically identifies a person’s face from a large image dataset or even from a live video. This thesis addresses facial recognition, a topic that has been widely studied due to its importance in many applications in both civilian and military domains. The application of face recognition systems has expanded from security purposes to social networking sites, managing fraud, and improving user experience. Numerous algorithms have been designed to perform face recognition with good accuracy. This problem is challenging due to the dynamic nature of the human face and the different poses that it can take. Regardless of the algorithm, facial recognition accuracy can be heavily affected by the presence of noise. This thesis presents a comparison of traditional and deep learning face recognition algorithms under the presence of noise. For this purpose, Gaussian and salt-andpepper noises are applied to the face images drawn from the ORL Dataset. The image recognition is performed using each of the following eight algorithms: principal component analysis (PCA), two-dimensional PCA (2D-PCA), linear discriminant analysis (LDA), independent component analysis (ICA), discrete cosine transform (DCT), support vector machine (SVM), convolution neural network (CNN) and Alex Net. The ORL dataset was used in the experiments to calculate the evaluation accuracy for each of the investigated algorithms. Each algorithm is evaluated with two experiments; in the first experiment only one image per person is used for training, whereas in the second experiment, five images per person are used for training. The investigated traditional algorithms are implemented with MATLAB and the deep learning algorithms approaches are implemented with Python. The results show that the best performance was obtained using the DCT algorithm with 92% dominant eigenvalues and 95.25 % accuracy, whereas for deep learning, the best performance was using a CNN with accuracy of 97.95%, which makes it the best choice under noisy conditions

Inferring Social Media Users’ Demographics from Profile Pictures: A Face++ Analysis on Twitter Users

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collection