Facial expression recognition with Convolutional Neural Networks

The Self-Organizing Map (SOM) algorithm has attracted a great deal of interest among researches and practitioners in a wide variety of fields. The SOM has been analyzed extensively, a number of variants have been developed and, perhaps most notably, it has been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 5384 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. The present addendum contains 2092 new articles, mainly from the years 1998-2002. We have provided a keyword index to help finding articles of interest, and additionally a modern automatically constructed variant of a thematic index: a WEBSOM interface to the whole article collection of years 1981-2000. The SOM of SOMs is available at http://websom.hut.fi/websom/somref/search.cgi for browsing and searching the collection.

Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum

Human face recognition based on multi-features using neural networks committee

In this paper, an efficient face recognition method using AGA and ANFIS-ABC has been proposed. At first stage, the face images gathered from the database are preprocessed. At Second stage, an interest point which is used to improve the detection rate consequently. The parameters used in the interest point determination are optimized using the Adaptive Genetic Algorithm. Finally using ANFIS, face images are classified by using extracted features. During the training process, the parameters of ANFIS are optimized using Artificial Bee Colony Algorithm (ABC) in order to improve the accuracy. The performance of the proposed ANFIS-ABC technique is evaluated using an ORL database with 400 images of 40 individuals, YALE-B database with 165 images of 15 individuals and finally with real time video the detection rate and false alarm rate is compared with proposed and existing methods to prove the system efficiency.

Interest point based face recognition using adaptive neuro fuzzy inference system

Visual Perception: Physiology, Psychology and Ecology, Vicki Bruce, Patrick Green. Lawrence Erlbaum, London (1985), xiii, +369. Price £8.95 (paperback)

The huge research effort in the field of face expression recognition (FER) technology is justified by the potential applications in multiple domains: computer science, engineering, psychology, neuroscience, to name just a few. Obviously, this generates an impressive number of scientific publications. The aim of this paper is to identify key representative approaches for facial expression recognition research in the past ten years (2003-2012).

Face expression recognition: A brief overview of the last decade

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

In this paper an improved version of an integrated environment that improves the mobility of blind persons into a limited area is presented. For the first time, the concept of the audio virtual realty is introduced here, as a man- machine interface between the subject and the wearable equipment that guides the movement. Other improvements are regarding the sensors used to acquire data from the surrounding environment: the 2D visual sensor has been replaced with a newer 3D visual sensor, while other types of sensors have been removed (an infrared sensor and a laser range finder). The obtained results and the further developments are also presented.

http://www.wseas.us/e-library/conferences/2007cscc/papers/561-369.pdf

Improved Version of an Integrated Environment for Assisted Movement of Visually Impaired

Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).

https://www.mdpi.com/1424-8220/21/12/4126/pdf

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis.

In this paper a face recognition method based on the interesting feature extraction technique and a committee of multilayer perceptron is described. The classifier consists of several independently trained neural networks. The final classification represents the combined response of individual networks.

Catalin Daniel Caleanu

Papers

Face expression recognition: A brief overview of the last decade

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Improved Version of an Integrated Environment for Assisted Movement of Visually Impaired

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis.

Facial recognition using committee of neural networks