A Face Recognition Method Using Deep Learning To Identify Mask And Unmask Objects

At the present, the use of face masks is growing day by day and it is mandated in most places across the world. People are encouraged to cover their faces when in public areas to avoid the spread of infection which can minimize the transmission of Covid-19 by 65 percent (according to the public health officials). So, it is important to detect people not wearing face masks. Additionally, face recognition has been applied to a wide area for security verification purposes since its performance, accuracy, and reliability [15] are better than any other traditional techniques like fingerprints, passwords, PINs, and so on. In recent years, facial recognition is becoming a challenging task because of various occlusions or masks like the existence of sunglasses, scarves, hats, and the use of make-up or disguise ingredients. So, the face recognition accuracy rate is affected by these types of masks. Moreover, the use of face masks has made conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, tracking school, and unlocking phones and laptops. As a result, we proposed a solution, Masked Facial Recognition (MFR) which can identify masked and unmasked people so individuals wearing a face mask do not need to take it out to authenticate themselves. We used the Deep Learning model, Inception ResNet V1 to train our model. The CASIA dataset [17] is applied for training images and the LFW (Labeled Faces in the Wild) dataset [18] with artificial marked faces are used for model evaluation purposes. The training and testing masked datasets are created using a Computer Vision-based approach (Dlib). We received an accuracy of around 96 percent for our three different trained models. As a result, the purposed work could be utilized effortlessly for both masked and unmasked face recognition and detection systems that are designed for safety and security verification purposes without any challenges

Tapaustutkimus joukkoliikenteen tasataksasta Helsingin seudulla

Thesis studies flat tariff as potential pricing model for Helsinki Region Transport. Flat tariff is compared to current zone model through financial analysis, user experiences, benchmarking other cities and evaluating effects on least well-off passengers. Thesis utilizes qualitative methods of expert, individual and group interviews and quantitative methods of price elasticity and trip/income analyses. Behavioural effects are recognized through theoretical framework. Results show that flat tariff is realizable but would demand increased subsidies. Experience of fairness relates with losses; if flat tariff is implemented with the current lowest price level, feeling of injustice should not occur. If price increases would be needed, negative emotions of “losers” are stronger than the joy of “winners”. Flat tariff with AB-zone price level would improve transport justice for all users. Flat tariff is not the optimal model to maximize both revenue and usage, unless the behavioural value for simplicity is expected to be high. However, defining the exact value of simplicity would demand further empirical preference studies. Behavioural eco-nomics is relevant framework for tariff planning, and planners need quantitative methods to combine psychological analysis and economical effects of pricing. In conclusion, thesis recommends remaining to zone model, but to lower prices of C- and D-zones in relation to AB-region.Diplomityö tutkii tasatariffia hinnoitteluvaihtoehtona Helsingin seudun liikenteessä. Tariffia verrataan vyöhykkeisiin rahoituksen, käyttäjäkokemusten ja muiden kaupunkien kokemusten kautta sekä arvioidaan vaikutuksia pienituloisille matkustajille. Tutkimus perustuu asiantuntija-, henkilö- ja ryhmähaastatteluiden laadulliseen analyysiin sekä hinta-joustojen ja matkojen kvantitatiiviseen analyysiin. Käyttäytymistaloustieteellisiä vaikutuksia analysoidaan teorian avulla. Tulosten perusteella tasatariffi on toteutettavissa, mutta edellyttää lisäsubventioita. Kokemus oikeudenmukaisuudesta liittyy hinnankorotuksiin; jos tasataksa toteutetaan ilman hinnankorotuksia, epäoikeudenmukaisuuden kokemus ei ole ongelma. Jos osalle käyttäjistä aiheutuu hinnankorotuksia, ”häviäjien” negatiiviset tunteet ovat voimakkaampia kuin “voittajien” tyytyväisyys. Tasatariffi nykyisellä AB-hintatasolla parantaisi liikkumisen oikeudenmukaisuutta kaikille käyttäjille. Tasataksa ei ole optimaalinen malli tulojen ja käytön maksimoimiseksi, ellei yksinkertaisuuden arvo asiakkaalle ole korkea. Yksinkertaisuuden arvon määrittäminen vaatisi kuitenkin empiirisiä preferenssitutkimuksia. Työ osoittaa käyttäytymistaloustieteen keskeisen roolin hinnoittelussa, ja suunnittelijoiden täytyy hallita kvantitatiiviset menetelmät hinnoittelun psykologisten ja taloudellisten vaikutusten analysoimiseksi. Johtopäätöksenä suositellaan pysyttäytymistä vyöhykemallissa ja CD-vyöhykkeiden hintojen laskua

Feature Selection Based on Sequential Orthogonal Search Strategy

This thesis introduces three new feature selection methods based on sequential orthogonal search strategy that addresses three different contexts of feature selection problem being considered. The first method is a supervised feature selection called the maximum relevance–minimum multicollinearity (MRmMC), which can overcome some shortcomings associated with existing methods that apply the same form of feature selection criterion, especially those that are based on mutual information. In the proposed method, relevant features are measured by correlation characteristics based on conditional variance while redundancy elimination is achieved according to multiple correlation assessment using an orthogonal projection scheme. The second method is an unsupervised feature selection based on Locality Preserving Projection (LPP), which is incorporated in a sequential orthogonal search (SOS) strategy. Locality preserving criterion has been proved a successful measure to evaluate feature importance in many feature selection methods but most of which ignore feature correlation and this means these methods ignore redundant features. This problem has motivated the introduction of the second method that evaluates feature importance jointly rather than individually. In the method, the first LPP component which contains the information of local largest structure (LLS) is utilized as a reference variable to guide the search for significant features. This method is referred to as sequential orthogonal search for local largest structure (SOS-LLS). The third method is also an unsupervised feature selection with essentially the same SOS strategy but it is specifically designed to be robust on noisy data. As limited work has been reported concerning feature selection in the presence of attribute noise, the third method is thus attempts to make an effort towards this scarcity by further exploring the second proposed method. The third method is designed to deal with attribute noise in the search for significant features, and kernel pre-images (KPI) based on kernel PCA are used in the third method to replace the role of the first LPP component as the reference variable used in the second method. This feature selection scheme is referred to as sequential orthogonal search for kernel pre-images (SOS-KPI) method. The performance of these three feature selection methods are demonstrated based on some comprehensive analysis on public real datasets of different characteristics and comparative studies with a number of state-of-the-art methods. Results show that each of the proposed methods has the capacity to select more efficient feature subsets than the other feature selection methods in the comparative studies