Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka

Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka

Smart Internet of Things Modular Micro Grow Room Architecture

This article proposes the Internet of Things-based self-sustaining modular grow room architecture for optimising the seed germination and seedling development process. The architecture is scalable and flexible as it can be adapted to particular environments, scopes, requirements and plant types; it is modular as the host room can contain one or more smaller-scale grow rooms, each of them controlling their own micro-environment independently. One of the main goals of the research was to develop such a system that could be deployed efficiently, with minimal costs and energy footprint, which would enable its practical usage primarily in private self-sustainable households. The usage of widely available and inexpensive components, open source code, and free cloud services all enabled us to reach such a goal. Besides simple automation mostly described by existing solutions, the architecture proposed within this article offers remote control and data processing and visualisation, data trend tracking, smart optimisation, and actuator control, and event notifications

Open Directory Project based universal taxonomy for Personalization of Online (Re)sources

Content personalization reflects the ability of content classification into (predefined) thematic units or information domains. Content nodes in a single thematic unit are related to a greater or lesser extent. An existing connection between two available content nodes assumes that the user will be interested in both resources (but not necessarily to the same extent). Such a connection (and its value) can be established through the process of automatic content classification and labeling. One approach for the classification of content nodes is the use of a predefined classification taxonomy. With the help of such classification taxonomy it is possible to automatically classify and label existing content nodes as well as create additional descriptors for future use in content personalization and recommendation systems. For these purposes existing web directories can be used in creating a universal, purely content based, classification taxonomy. This work analyzes Open Directory Project (ODP) web directory and proposes a novel use of its structure and content as the basis for such a classification taxonomy. The goal of a unified classification taxonomy is to allow for content personalization from heterogeneous sources. In this work we focus on the overall quality of ODP as the basis for such a classification taxonomy and the use of its hierarchical structure for automatic labeling. Due to the structure of data in ODP different grouping schemes are devised and tested to find the optimal content and structure combination for a proposed classification taxonomy as well as automatic labeling processes. The results provide an in-depth analysis of ODP and ODP based content classification and automatic labeling models. Although the use of ODP is well documented, this question has not been answered to date

Klasifikacija dvodeminezionalnih slika lica za razlikovanje djece od odraslih osoba na temelju antropometrije

Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.Slike lica mogu biti klasificirane na različite načine. Ovo istraživanje koristi dvodimenzionalne fotografije ljudskih lica za detekciju djece na slikama. Kreiran je novi algoritam za klasifikaciju fotografija ljudskih lica u dvije grupe, djeca i odrasli. Algoritam će se također koristiti za procjenu dobi osoba na slici te će biti analizirani postojeći algoritmi. Kroz analizu literature o karakterističnim točkama korištenih u procjeni dobi i kombinacijom dobivenih karakterističnih točaka s morfološkim promjenama tokom odrastanja i starenja, definirane su karakteristične točke potrebne za klasifikaciju i procjenu dobi. Algoritam se bazira na omjerima Euklidskih udaljenosti između identificiranih karakterističnih točaka

Automatic Ethnicity Classification from Middle Part of the Face Using Convolutional Neural Networks

In the field of face biometrics, finding the identity of a person in an image is most researched, but there are other, soft biometric information that are equally as important, such as age, gender, ethnicity or emotion. Nowadays, ethnicity classification has a wide application area and is a prolific area of research. This paper gives an overview of recent advances in ethnicity classification with focus on convolutional neural networks (CNNs) and proposes a new ethnicity classification method using only the middle part of the face and CNN. The paper also compares the differences in results of CNN with and without plotted landmarks. The proposed model was tested using holdout testing method on UTKFace dataset and FairFace dataset. The accuracy of the model was 80.34% for classification into five classes and 61.74% for classification into seven classes, which is slightly better than state-of-the-art, but it is also important to note that results in this paper are obtained by using only the middle part of the face which reduces the time and resources necessary