Explore the vast range of titles available.

\"Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field\"-- Provided by publisher.

Rationale 3,4-Methylenedioxymethamphetamine (MDMA) is used recreationally and investigated as an adjunct to psychotherapy. Methylphenidate and modafinil are psychostimulants that are used to treat attention-deficit/hyperactivity disorder and narcolepsy, respectively, but they are also misused as cognitive enhancers. Little is known about differences in the acute effects of equally cardiostimulant doses of these stimulant-type substances compared directly within the same subjects. Methods We investigated the acute autonomic, subjective, endocrine, and emotional effects of single doses of MDMA (125 mg), methylphenidate (60 mg), modafinil (600 mg), and placebo in a double-blind, cross-over study in 24 healthy participants. Acute drug effects were tested using psychometric scales, the Facial Emotion Recognition Task (FERT), and the Sexual Arousal and Desire Inventory (SADI). Results All active drugs produced comparable hemodynamic and adverse effects. MDMA produced greater increases in pupil dilation, subjective good drug effects, drug liking, happiness, trust, well-being, and alterations in consciousness than methylphenidate or modafinil. Only MDMA reduced subjective anxiety and impaired fear recognition and led to misclassifications of emotions as happy on the FERT. On the SADI, only MDMA produced sexual arousal-like effects. Only MDMA produced marked increases in cortisol, prolactin, and oxytocin. In contrast to MDMA, methylphenidate increased subjective anxiety, and methylphenidate and modafinil increased misclassifications of emotions as angry on the FERT. Modafinil had no significant subjective drug effects but significant sympathomimetic and adverse effects. Conclusions MDMA induced subjective, emotional, sexual, and endocrine effects that were clearly distinct from those of methylphenidate and modafinil at the doses used.

The neuropeptide oxytocin (OXT) is thought to attenuate anxiety by dampening amygdala reactivity to threat in individuals with generalized social anxiety disorder (GSAD). Because the brain is organized into networks of interconnected areas, it is likely that OXT impacts functional coupling between the amygdala and other socio-emotional areas of the brain. Therefore, the aim of the current study was to examine the effects of OXT on amygdala functional connectivity during the processing of fearful faces in GSAD subjects and healthy controls (HCs). In a randomized, double-blind, placebo (PBO)-controlled, within-subjects design, 18 HCs and 17 GSAD subjects performed a functional magnetic resonance imaging task designed to probe amygdala response to fearful faces following acute intranasal administration of PBO or OXT. Functional connectivity between the amygdala and the rest of the brain was compared between OXT and PBO sessions using generalized psychophysiological interaction analyses. Results indicated that within individuals with GSAD, but not HCs, OXT enhanced functional connectivity between the amygdala and the bilateral insula and middle cingulate/dorsal anterior cingulate gyrus during the processing of fearful faces. These findings suggest that OXT may have broad pro-social implications such as enhancing the integration and modulation of social responses.

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

•Open-source face recognition matched up to 59 % of participants’ photos to MRIs.•Commercial face recognition matched up to 98 % of participants’ photos to MRIs.•Re-identification is also feasible even by individuals with only free tools.•Further supports the need to remove or replace face imagery in shared brain images. In recent years facial recognition software has gone from an area of research to widespread adoption and broad public availability. Open-source face recognition packages are freely available on the internet for anyone to download, and several public websites allow users to run facial recognition on photos without needing any technical knowledge or equipment beyond internet access, making facial recognition accessible for anyone to use for any purpose. Previous research has demonstrated the ability of commercial software to identify a person based on facial content in brain imaging. In this study we tested two commercial facial recognition programs and a variety of popular open-source computer vision and facial recognition software packages to measure how accurately they could be used for reidentification of research participants in brain imaging studies. We tested a “population to sample” threat model, measuring the rates of success for which face recognition software selected the correct MRI-based face reconstruction from a set of 182 participants as its top-scoring match for input facial photographs. We found that the freely available open-source software packages we tested can reidentify a research participant with up to 59 % accuracy. This was less than the commercial packages, which were able to achieve much higher accuracies in the ranges of 92 % and 98 % in identical testing scenarios, but it demonstrates the feasibility of re-identifying faces in research MRI even by individuals with access to only freely available software. As the trust and confidence of potential participants is essential to brain imaging research, especially with widespread and mandated data-sharing of brain scans, this further supports the need to replace identifiable face imagery in brain images to protect the privacy of research participants.

Facial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.

RationaleIndividuals with music performance anxiety (MPA) present physical, behavioral, and cognitive manifestations of anxiety, in addition to information processing deficits, especially in facial emotion recognition (FER).ObjectivesTo assess the effects of a single dose of intranasal oxytocin (24 IU) on FER in a sample of musicians with high and low MPA (primary outcome), as well as indicators of mood/anxiety and self-assessed performance (secondary outcomes).MethodsCrossover, randomized, double-blind, placebo-controlled trial involving 43 male musicians with different levels of MPA. Participants completed a static facial emotion recognition task and self-rated mood and performance scales. Data were analyzed using ANOVA 2 × 0 for crossover trials and the Omnibus test (measure of separability between intervention and carryover effects).ResultsOnly musicians with high MPA treated with oxytocin had a higher accuracy in the recognition of happiness (p < 0.03; d > 0.72). No effects of oxytocin were found on mood indicators or on self-perceived performance, regardless of MPA level.ConclusionsThe results indicate possible benefits of the acute treatment with oxytocin in MPA, which may improve the management of this common and disabling condition that affects professional musicians. The appropriate perception of positive feedback may increase confidence and feelings of social acceptance, reducing symptoms associated with the condition. The lack of effects on mood/anxiety and cognition may be explained by the context-dependent characteristic of the effects of oxytocin, since the experiment did not represent an actual situation of social threat.Trial registrationBrazilian Clinical Trials Registry (Registro Brasileiro de Ensaios Clínicos): No. RBR-9cph2q

Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.

Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because they allow both involved individuals to use the same document. Several algorithms are currently being developed to detect Morphing Attacks, often requiring large data sets of morphed face images for training. In the present study, face embeddings are used for two different purposes: first, to pre-select images for the subsequent large-scale generation of Morphing Attacks, and second, to detect potential Morphing Attacks. Previous studies have demonstrated the power of embeddings in both use cases. However, we aim to build on these studies by adding the more powerful MagFace model to both use cases, and by performing comprehensive analyses of the role of embeddings in pre-selection and attack detection in terms of the vulnerability of face recognition systems and attack detection algorithms. In particular, we use recent developments to assess the attack potential, but also investigate the influence of morphing algorithms. For the first objective, an algorithm is developed that pairs individuals based on the similarity of their face embeddings. Different state-of-the-art face recognition systems are used to extract embeddings in order to pre-select the face images and different morphing algorithms are used to fuse the face images. The attack potential of the differently generated morphed face images will be quantified to compare the usability of the embeddings for automatically generating a large number of successful Morphing Attacks. For the second objective, we compare the performance of the embeddings of two state-of-the-art face recognition systems with respect to their ability to detect morphed face images. Our results demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Various open-source and commercial-off-the-shelf face recognition systems are vulnerable to the generated Morphing Attacks, and their vulnerability increases when image pre-selection is based on embeddings compared to random pairing. In particular, landmark-based closed-source morphing algorithms generate attacks that pose a high risk to any tested face recognition system. Remarkably, more accurate face recognition systems show a higher vulnerability to Morphing Attacks. Among the systems tested, commercial-off-the-shelf systems were the most vulnerable to Morphing Attacks. In addition, MagFace embeddings stand out as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the benefits of face embeddings for more effective image pre-selection for face morphing and for more accurate detection of morphed face images, as demonstrated by extensive analysis of various designed attacks. The MagFace model is a powerful alternative to the often-used ArcFace model in detecting attacks and can increase performance depending on the use case. It also highlights the usability of embeddings to generate large-scale morphed face databases for various purposes, such as training Morphing Attack Detection algorithms as a countermeasure against attacks.

Blunted facial affect is a common negative symptom of schizophrenia. Additionally, assessing the trustworthiness of faces is a social cognitive ability that is impaired in schizophrenia. Currently available pharmacological agents are ineffective at improving either of these symptoms, despite their clinical significance. The hypothalamic neuropeptide oxytocin has multiple prosocial effects when administered intranasally to healthy individuals and shows promise in decreasing negative symptoms and enhancing social cognition in schizophrenia. Although two small studies have investigated oxytocin's effects on ratings of facial trustworthiness in schizophrenia, its effects on facial expressivity have not been investigated in any population. We investigated the effects of oxytocin on facial emotional expressivity while participants performed a facial trustworthiness rating task in 33 individuals with schizophrenia and 35 age-matched healthy controls using a double-blind, placebo-controlled, cross-over design. Participants rated the trustworthiness of presented faces interspersed with emotionally evocative photographs while being video-recorded. Participants' facial expressivity in these videos was quantified by blind raters using a well-validated manualized approach (i.e. the Facial Expression Coding System; FACES). While oxytocin administration did not affect ratings of facial trustworthiness, it significantly increased facial expressivity in individuals with schizophrenia (Z = -2.33, p = 0.02) and at trend level in healthy controls (Z = -1.87, p = 0.06). These results demonstrate that oxytocin administration can increase facial expressivity in response to emotional stimuli and suggest that oxytocin may have the potential to serve as a treatment for blunted facial affect in schizophrenia.