FEATURE-BASED FACE DETECTION: A SURVEY

Human and computer vision has a vital role in intelligent interaction with computer, face recognition is one of the subjects that have a wide area in researches, a big effort has been exerted in last decades for face recognition, face detection, face tracking, as yet new algorithms for building fully automated system are required, these algorithms should be robust and efficient. The first step of any face recognition system is face detection, the goal of face detection is the extraction of face region within image, taking into consideration lightning, orientation and pose variation, whenever this step accurate the result of face recognition will be better, this paper introduce a survey of techniques and methods of feature based face detection

Identificació no-supervisada de persones en programes de televisió

En els darrers anys la quantitat de dades de vídeos i imatges ha anat augmentant, això ha provocat diversos problemes d’anotació i classificació del conjunt de dades. Un d’aquests grans problemes és la identificació de persones en vídeos, és per això que la recerca en aquest àmbit ha incrementat en els últims anys. L’objectiu d’aquest projecte és trobar un nou algoritme per poder millorar la identificació no supervisada de persones en seqüències de vídeo per programes de televisió. Per poder dur a terme aquesta millora es crearà un nou classificador des de cero per poder identificar si una persona en una seqüència de vídeo està parlant o no. Aquest classificador serà creat a partir de l’extracció de les cares de persones en diferents vídeos, i classificant-les manualment respecte si estan parlant o no. A partir d’aquest conjunt de dades, es detectaran les boques i es mesurarà la distància entre els llavis, per tal de poder crear un vector de distàncies per cada cara detectada. A més a més, s’aplicarà un detector facial millorat i es compararan els resultats amb un altre detector més antic. Finalment s’exposaran els resultats un cop aplicat el nou classificado

Learning Representations for Face Recognition: A Review from Holistic to Deep Learning

For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy

REGULATING FEDERAL LAW ENFORCEMENT’S USE OF FACIAL RECOGNITION TECHNOLOGY

This research examines optimal regulation allowing federal law enforcement to use facial recognition technology (FRT) while protecting civil liberties. Through a literature review, a comparative analysis of the European Union’s Law Enforcement Directive (LED), and a policy analysis for U.S. regulation it evaluates frameworks balancing effectiveness and proportionality. Findings show that comprehensive legislation upholding fairness, accountability, and purpose limitations, complemented by independent auditing and oversight, can enable public safety benefits while constraining unfettered use. However, flexibility is imperative; legal, ethical, and technical dimensions remain uncertain. The research concludes that nuanced, principled governance provides the most prudent path. The next steps involve ongoing stakeholder engagement, implementation planning, and impact evaluation to refine balanced oversight as case law, technology, and societal norms evolve. This analysis fills a gap by evaluating policy tradeoffs—equipping stakeholders with evidence to inform sound FRT governance. With adaptable oversight, facial recognition can be steered toward just ends.Approved for public release. Distribution is unlimited.Civilian, Department of Homeland Securit

A First Look Into Users’ Perceptions of Facial Recognition in the Physical World

Facial recognition (FR) technology is being adopted in both private and public spheres for a wide range of reasons, from ensuring physical safety to providing personalized shopping experiences. It is not clear yet, though, how users perceive this emerging technology in terms of usefulness, risks, and comfort. We begin to address these questions in this paper. In particular, we conducted a vignette-based study with 314 participants on Amazon Mechanical Turk to investigate their perceptions of facial recognition in the physical world, based on thirty-five scenarios across eight different contexts of FR use. We found that users do not have a binary answer towards FR adoption. Rather, their perceptions are grounded in the specific contexts in which FR will be applied. The participants considered a broad range of factors, including control over facial data, the utility of FR, the trustworthiness of organizations using FR, and the location and surroundings of FR use to place the corresponding privacy risks in context. They weighed the privacy risks with the usability, security, and economic gain of FR use as they reported their perceptions. Participants also noted the reasons and rationals behind their perceptions of facial recognition, which let us conduct an in-depth analysis of their perceived benefits, concerns, and comfort with using this technology in various scenarios. Through this first systematic look into users’ perceptions of facial recognition in the physical world, we shed light on the tension between FR adoption and users’ concerns. Taken together, our findings have broad implications that advance the Privacy and Security community’s understanding of FR through the lens of users, where we presented guidelines for future research in these directions

Um estudo de técnicas de detecção de olhos utilizando Visão Computacional

TCC (graduação) - Universidade Federal de Santa Catarina. Campus Joinville. Engenharia Mecatrônica.Todo ano, um número significativo de motoristas se envolvem em acidentes no trânsito por problemas relacionados à fadiga e sonolência. Neste sentido, é importante investigar técnicas que auxiliem na prevenção desse tipo de acidente. A área de Visão Computacional permite a análise de imagens para a extração de informações com relação a essas imagens podendo, por exemplo, extrair aspectos visuais com relação a um motorista objetivando prevenir possíveis problemas associados. Levando isso em consideração, este trabalho estudou técnicas de detecção de olhos, para identificar se os olhos estão abertos ou fechados, etapa que pode ser utilizada para posterior identificação de sonolência em motoristas. Os algoritmos desenvolvidos utilizam as técnicas de detecção Viola e Jones, Histogramas de Gradientes Orientados e Transformada de Hough. Os algoritmos foram feitos em linguagem de programação Python utilizando as bibliotecas OpenCV e Dlib. Este trabalho faz a análise dos resultados alcançados e compara o desempenho das técnicas entre si levando em consideração custo computacional, uso de memória e acurácia na detecção.Every year, a significant number of drivers get involved in traffic accidents due to problems related to fatigue and drowsiness. Due to this, it is important to investigate techniques that help prevent this type of accident. The Computer Vision field enables the analysis of images to extract information regarding these images and, for example, extract visual aspects regarding a driver in order to prevent possible associated problems. Taking this into consideration, this paper has studied eye detection techniques to identify whether the eyes are open or closed, a step that can be used for later identification of drowsiness in drivers. The developed algorithms use Viola and Jones, Hough Transform and Histograms of Oriented Gradients detection techniques. The algorithms were made in Python programming language using the OpenCV and Dlib libraries. This paper analyzes the achieved results and compares the performance of the techniques against each other taking into account computational cost, memory usage and detection accuracy

Portraits of Automated Facial Recognition

Automated facial recognition algorithms are increasingly intervening in society. This book offers a unique analysis of these algorithms from a critical visual culture studies perspective. The first part of this study examines the example of an early facial recognition algorithm called »eigenface« and traces a history of the merging of statistics and vision. The second part addresses contemporary artistic engagements with facial recognition technology in the work of Thomas Ruff, Zach Blas, and Trevor Paglen. This book argues that we must take a closer look at the technology of automated facial recognition and claims that its forms of representation are embedded with visual politics. Even more significantly, this technology is redefining what it means to see and be seen in the contemporary world

The New Aesthetic and Art: Constellations of the Postdigital

The case for the new aesthetic -- Manifestations of the new aesthetic -- Glitch ontology and the new aesthetic -- Setting the stage : the new precursorsand boundaries for a new aesthetic art -- Letting go : new aesthetic artists and the new aesthetic art that works -- Teleology and the new aesthetic -- Conclusion -- References -- Biographies. The new aesthetic and art: constellations of the postdigital is an interdisciplinary analysis focusing on new digital phenomena at the intersections of theory andcontemporary art. Asserting the unique character of New Aesthetic objects, Contreras-Koterbay and Mirocha trace the origins of the New Aesthetic in visual arts, design, and software, find its presence resonating in various kinds of digital imagery, and track its agency in everyday effects of the intertwined physical world and the digital realm. Contreras-Koterbay and Mirocha bring to light an original perspective that identifies an autonomous quality in common digital objects and examples ofart that are increasingly an important influence for today\u27s culture and society.https://dc.etsu.edu/etsu_books/1118/thumbnail.jp

Optimisation des Systèmes Multimodaux pour l’Identification dans l’Imagerie

Parmi les médias les plus populaires qui ont pris une place incontournable pour le développement des systèmes de reconnaissances biométriques en général et les systèmes de la reconnaissance de visage en particulier on trouve l’Image. L’une des utilisations les plus courantes des images est l’identification/vérification en biométrie qui connaît un intérêt grandissant depuis quelques années. L’efficacité des techniques d’identification en imagerie est aujourd’hui très fortement liée à des contraintes fortes imposées à l’utilisateur. Une voie de recherche actuelle se tourne donc vers la gestion de situations où l’acquisition des données est moins contrainte. Finalement, l’usage d’une seule modalité est souvent limité en termes de performance ou de difficultés d’usage, c’est pourquoi il apparaît intéressant d’évaluer l’apport de la multi-modalité dans ce contexte. L’objectif de la thèse est de mener un travail pour poursuivre une recherche tournée à la fois vers les techniques d’optimisation basées d’une part sur les descripteurs hybrides et les patchs ainsi que leurs techniques de fusions, et d’autre part sur le Deep Learning (Transfer Learning). Nous nous intéressons plus particulièrement à l’image du visage et nos approches sont validées sur plusieurs bases de données universelles pour défier tous les aléas d’acquisition et d’environnements non contrôlés

Evaluating reasoning heuristics for a hybrid theorem proving platform

Text in English with abstracts in English, Afrikaans and isiZuluThe formalisation of first-order logic and axiomatic set theory in the first half of the 20th century—along with the advent of the digital computer—paved the way for the development of automated theorem proving. In the 1950s, the automation of proof developed from proving elementary geometric problems and finding direct proofs for problems in Principia Mathematica by means of simple, human-oriented rules of inference. A major advance in the field of automated theorem proving occurred in 1965, with the formulation of the resolution inference mechanism. Today, powerful Satisfiability Modulo Theories (SMT) provers combine SAT solvers with sophisticated knowledge from various problem domains to prove increasingly complex theorems. The combinatorial explosion of the search space is viewed as one of the major challenges to progress in the field of automated theorem proving. Pioneers from the 1950s and 1960s have already identified the need for heuristics to guide the proof search effort. Despite theoretical advances in automated reasoning and technological advances in computing, the size of the search space remains problematic when increasingly complex proofs are attempted. Today, heuristics are still useful and necessary to discharge complex proof obligations. In 2000, a number of heuristics was developed to aid the resolution-based prover OTTER in finding proofs for set-theoretic problems. The applicability of these heuristics to next-generation theorem provers were evaluated in 2009. The provers Vampire and Gandalf required respectively 90% and 80% of the applicable OTTER heuristics. This dissertation investigates the applicability of the OTTER heuristics to theorem proving in the hybrid theorem proving environment Rodin—a system modelling tool suite for the Event-B formal method. We show that only 2 of the 10 applicable OTTER heuristics were useful when discharging proof obligations in Rodin. Even though we argue that the OTTER heuristics were largely ineffective when applied to Rodin proofs, heuristics were still needed when proof obligations could not be discharged automatically. Therefore, we propose a number of our own heuristics targeted at theorem proving in the Rodin tool suite.Die formalisering van eerste-orde-logika en aksiomatiese versamelingsteorie in die eerste helfte van die 20ste eeu, tesame met die koms van die digitale rekenaar, het die weg vir die ontwikkeling van geoutomatiseerde bewysvoering gebaan. Die outomatisering van bewysvoering het in die 1950’s ontwikkel vanuit die bewys van elementêre meetkundige probleme en die opspoor van direkte bewyse vir probleme in Principia Mathematica deur middel van eenvoudige, mensgerigte inferensiereëls. Vooruitgang is in 1965 op die gebied van geoutomatiseerde bewysvoering gemaak toe die resolusie-inferensie-meganisme geformuleer is. Deesdae kombineer kragtige Satisfiability Modulo Theories (SMT) bewysvoerders SAT-oplossers met gesofistikeerde kennis vanuit verskeie probleemdomeine om steeds meer komplekse stellings te bewys. Die kombinatoriese ontploffing van die soekruimte kan beskou word as een van die grootste uitdagings vir verdere vooruitgang in die veld van geoutomatiseerde bewysvoering. Baanbrekers uit die 1950’s en 1960’s het reeds bepaal dat daar ’n behoefte is aan heuristieke om die soektog na bewyse te rig. Ten spyte van die teoretiese vooruitgang in outomatiese bewysvoering en die tegnologiese vooruitgang in die rekenaarbedryf, is die grootte van die soekruimte steeds problematies wanneer toenemend komplekse bewyse aangepak word. Teenswoordig is heuristieke steeds nuttig en noodsaaklik om komplekse bewysverpligtinge uit te voer. In 2000 is ’n aantal heuristieke ontwikkel om die resolusie-gebaseerde bewysvoerder OTTER te help om bewyse vir versamelingsteoretiese probleme te vind. Die toepaslikheid van hierdie heuristieke vir die volgende generasie bewysvoerders is in 2009 geëvalueer. Die bewysvoerders Vampire en Gandalf het onderskeidelik 90% en 80% van die toepaslike OTTER-heuristieke nodig gehad. Hierdie verhandeling ondersoek die toepaslikheid van die OTTER-heuristieke op bewysvoering in die hibriede bewysvoeringsomgewing Rodin—’n stelselmodelleringsuite vir die formele Event-B-metode. Ons toon dat slegs 2 van die 10 toepaslike OTTER-heuristieke van nut was vir die uitvoering van bewysverpligtinge in Rodin. Ons voer aan dat die OTTER-heuristieke grotendeels ondoeltreffend was toe dit op Rodin-bewyse toegepas is. Desnieteenstaande is heuristieke steeds nodig as bewysverpligtinge nie outomaties uitgevoer kon word nie. Daarom stel ons ’n aantal van ons eie heuristieke voor wat in die Rodin-suite aangewend kan word.Ukwenziwa semthethweni kwe-first-order logic kanye ne-axiomatic set theory ngesigamu sokuqala sekhulunyaka lama-20—kanye nokufika kwekhompyutha esebenza ngobuxhakaxhaka bedijithali—kwavula indlela ebheke ekuthuthukisweni kwenqubo-kusebenza yokufakazela amathiyoremu ngekhomyutha. Ngeminyaka yawo-1950, ukuqinisekiswa kobufakazi kwasuselwa ekufakazelweni kwezinkinga zejiyomethri eziyisisekelo kanye nasekutholakaleni kobufakazi-ngqo bezinkinga eziphathelene ne-Principia Mathematica ngokuthi kusetshenziswe imithetho yokuqagula-sakucabangela elula, egxile kubantu. Impumelelo enkulu emkhakheni wokufakazela amathiyoremu ngekhompyutha yenzeka ngowe-1965, ngokwenziwa semthethweni kwe-resolution inference mechanism. Namuhla, abafakazeli abanohlonze bamathiyori abizwa nge-Satisfiability Modulo Theories (SMT) bahlanganisa ama-SAT solvers nolwazi lobungcweti oluvela kwizizinda zezinkinga ezihlukahlukene ukuze bakwazi ukufakazela amathiyoremu okungelula neze ukuwafakazela. Ukukhula ngesivinini kobunzima nobunkimbinkimbi benkinga esizindeni esithile kubonwa njengenye yezinselelo ezinkulu okudingeka ukuthi zixazululwe ukuze kube nenqubekela phambili ekufakazelweni kwamathiyoremu ngekhompyutha. Amavulandlela eminyaka yawo-1950 nawo-1960 asesihlonzile kakade isidingo sokuthi amahuristikhi (heuristics) kube yiwona ahola umzamo wokuthola ubufakazi. Nakuba ikhona impumelelo esiyenziwe kumathiyori ezokucabangela okujulile kusetshenziswa amakhompyutha kanye nempumelelo yobuchwepheshe bamakhompyutha, usayizi wesizinda usalokhu uyinkinga uma kwenziwa imizamo yokuthola ubufakazi obuyinkimbinkimbi futhi obunobunzima obukhudlwana. Namuhla imbala, amahuristikhi asewuziso futhi ayadingeka ekufezekiseni izibopho zobufakazi obuyinkimbinkimbi. Ngowezi-2000, kwathuthukiswa amahuristikhi amaningana impela ukuze kulekelelwe uhlelo-kusebenza olungumfakazeli osekelwe phezu kwesixazululo, olubizwa nge-OTTER, ekutholeni ubufakazi bama-set-theoretic problems. Ukusebenziseka kwalawa mahuristikhi kwizinhlelo-kusebenza ezingabafakazeli bamathiyoremu besimanjemanje kwahlolwa ngowezi-2009. Uhlelo-kusebenza olungumfakazeli, olubizwa nge-Vampire kanye nalolo olubizwa nge-Gandalf zadinga ama-90% kanye nama-80%, ngokulandelana kwazo, maqondana nama-OTTER heuristics afanelekile. Lolu cwaningo luphenya futhi lucubungule ukusebenziseka kwama-OTTER heuristics ekufakazelweni kwamathiyoremu esimweni esiyinhlanganisela sokufakazela amathiyoremu esibizwa nge-Rodin—okuyi-system modelling tool suite eqondene ne-Event-B formal method. Kulolu cwaningo siyabonisa ukuthi mabili kuphela kwayi-10 ama-OTTER heuristics aba wusizo ngenkathi kufezekiswa isibopho sobufakazi ku-Rodin. Nakuba sibeka umbono wokuthi esikhathini esiningi ama-OTTER heuristics awazange abe wusizo uma esetshenziswa kuma-Rodin proofs, amahuristikhi asadingeka ezimweni lapho izibopho zobufakazi zingazenzekelanga ngokwazo ngokulawulwa yizinhlelo-kusebenza zekhompyutha. Ngakho-ke, siphakamisa amahuristikhi ethu amaningana angasetshenziswa ekufakazeleni amathiyoremu ku-Rodin tool suite.School of ComputingM. Sc. (Computer Science