How to Do Things With Words

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.

https://www.mdpi.com/1424-8220/20/2/342/pdf

Face Recognition Systems: A Survey

FNDNet – A deep convolutional neural network for fake news detection

Sentiment analysis is a fast growing area of research in natural language processing (NLP) and text classifications. This technique has become an essential part of a wide range of applications including politics, business, advertising and marketing. There are various techniques for sentiment analysis, but recently word embeddings methods have been widely used in sentiment classification tasks. Word2Vec and GloVe are currently among the most accurate and usable word embedding methods which can convert words into meaningful vectors. However, these methods ignore sentiment information of texts and need a large corpus of texts for training and generating exact vectors. As a result, because of the small size of some corpora, researcher often have to use pre-trained word embeddings which were trained on other large text corpora such as Google News with about 100 billion words. The increasing accuracy of pre-trained word embeddings has a great impact on sentiment analysis research. In this paper, we propose a novel method, Improved Word Vectors (IWV), which increases the accuracy of pre-trained word embeddings in sentiment analysis. Our method is based on Part-of-Speech (POS) tagging techniques, lexicon-based approaches, word position algorithm and Word2Vec/GloVe methods. We tested the accuracy of our method via different deep learning models and benchmark sentiment datasets. Our experiment results show that Improved Word Vectors (IWV) are very effective for sentiment analysis.

Sentiment analysis based on improved pre-trained word embeddings

The identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

Fog Computing Based Face Identification and Resolution Scheme in Internet of Things

http://home.zcu.cz/~pkral/papers/cee_kral15.pdf

Automatic face recognition system based on the SIFT features

Cancer is nowadays considered as one of the most dangerous diseases in the world. Especially, breast cancer represents for women the second most common type of cancer and is a main cause of cancer dead. This paper presents a novel method for breast cancer detection from mammographic images based on Local Binary Patterns (LBP). This approach successfully uses LBP based features with a classifier and thresholding. The proposed method is evaluated on a set composed of images extracted from MIAS and DDSM databases. We have experimentally shown that the proposed method is efficient and effective because the achieved accuracy is about 84%.

LBP features for breast cancer detection

The objective of this paper is to introduce a novel face database. It is composed of face images taken in real-world conditions and is freely available for research purposes at http://ufi.kiv.zcu.cz. We have created this dataset in order to facilitate to researchers a straightforward comparison and evaluation of their face recognition approaches under “very difficult” conditions. It is composed of two partitions. The first one, called Cropped images, contains automatically detected faces from photographs. The number of individuals is 605. These images are cropped and resized to have approximately the same face size. Images in the second partition, called Large images, contain not only faces, however some background objects are also present. Therefore, it is necessary to include the face detection task before the face recognition itself. This partition contains images of 530 individuals. Another contribution of this paper is to show the recognition accuracy of several state-of-the-art face recognition approaches on this dataset to provide a baseline score for further research.

/pdf/unconstrained-facial-images-database-for-face-recognition-4cymiuaorg.pdf

Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions

Dialogue act recognition is an important component of a large number of natural language processing pipelines. Many research works have been carried out in this area, but relatively few investigate deep neural networks and word embeddings. This is surprising, given that both of these techniques have proven exceptionally good in most other language-related domains. We propose in this work a new deep neural network that explores recurrent models to capture word sequences within sentences, and further study the impact of pretrained word embeddings. We validate this model on three languages: English, French and Czech. The performance of the proposed approach is consistent across these languages and it is comparable to the state-of-the-art results in English. More importantly, we confirm that deep neural networks indeed outperform a Maximum Entropy classifier, which was expected. However , and this is more surprising, we also found that standard word2vec em-beddings do not seem to bring valuable information for this task and the proposed model, whatever the size of the training corpus is. We thus further analyse the resulting embeddings and conclude that a possible explanation may be related to the mismatch between the type of lexical-semantic information captured by the word2vec embeddings, and the kind of relations between words that is the most useful for the dialogue act recognition task.

On the Effects of Using word2vec Representations in Neural Networks for Dialogue Act Recognition

As the number of digitized historical documents has increased rapidly during the last a few decades, it is necessary to provide efficient methods of information retrieval and knowledge extraction to make the data accessible. 
Such methods are dependent on optical character recognition (OCR) which converts the document images into textual representations. Nowadays, OCR methods are often not adapted to the historical domain; moreover, they usually need a significant amount of annotated documents. Therefore, this paper introduces a set of methods that allows performing an OCR on historical document images using only a small amount of real, manually annotated training data. The presented complete OCR system includes two main tasks: page layout analysis including text block and line segmentation and OCR. Our segmentation methods are based on fully convolutional networks, and the OCR approach utilizes recurrent neural networks. Both approaches are state of the art in the relevant fields. We have created a novel real dataset for OCR from Porta fontium portal. This corpus is freely available for research, and all proposed methods are evaluated on these data. We show that both the segmentation and OCR tasks are feasible with only a few annotated real data samples. The experiments aim at determining the best way how to achieve good performance with the given small set of data. We also demonstrate that obtained scores are comparable or even better than the scores of several state-of-the-art systems. To sum up, this paper shows a way how to create an efficient OCR system for historical documents with a need for only a little annotated training data.

/pdf/building-an-efficient-ocr-system-for-historical-documents-ggak4x4vuk.pdf

Ladislav Lenc

Papers

Automatic face recognition system based on the SIFT features

LBP features for breast cancer detection

Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions

On the Effects of Using word2vec Representations in Neural Networks for Dialogue Act Recognition

Building an efficient OCR system for historical documents with little training data