Ladislav Lenc

Automatic face recognition system based on the SIFT features

We proposed and implemented a new face corpus creation algorithm.We created a new facial corpus from the data of the Czech News Agency.We evaluated a novel face recognition method, the SIFT based Kepenekci approach.We proposed and evaluated two novel confidence measure techniques.We proposed, implemented and evaluated the fully automatic face recognition system. The main goal of this paper is to propose and implement an experimental fully automatic face recognition system which will be used to annotate photographs during insertion into a database. Its main strength is to successfully process photos of a great number of different individuals taken in a totally uncontrolled environment. The system is available for research purposes for free. It uses our previously proposed SIFT based Kepenekci approach for the face recognition, because it outperforms a number of efficient face recognition approaches on three large standard corpora (namely FERET, AR and LFW). The next goal is proposing a new corpus creation algorithm that extracts the faces from the database and creates a facial corpus. We show that this algorithm is beneficial in a preprocessing step of our system in order to create good quality face models. We further compare the performance of our SIFT based Kepenekci approach with the original Kepenekci method on the created corpus. This comparison proves that our approach significantly outperforms the original one. The last goal is to propose two novel supervised confidence measure methods based on a posterior class probability and a multi-layer perceptron to identify incorrectly recognized faces. These faces are then removed from the recognition results. We experimentally validated that the proposed confidence measures are very efficient and thus suitable for our task.

Automatically Detected Feature Positions for LBP Based Face Recognition

This paper presents a novel approach for automatic face recognition based on the Local Binary Patterns (LBP). One drawback of the current LBP based methods is that the feature positions are fixed and thus do not reflect the properties of the particular images. We propose to solve this issue by a method that automatically detects feature positions in the image. These key-points are determined using the Gabor wavelet transform and k-means clustering algorithm. The proposed method is evaluated on two corpora: AT&T Database of Faces and our Czech News Agency (CTK) dataset containing uncontrolled face images. The recognition rate on the first dataset is 99.5% which represents 2.5% improvement compared to the original LBP method. The best recognition rate obtained on the CTK corpus is 59.1% whereas the original LBP method reaches only 38.1%.

Novel Matching Methods for Automatic Face Recognition Using SIFT

The object of interest of this paper is Automatic Face Recognition (AFR). The usual methods need a labeled corpus and the number of training examples plays a crucial role for the recognition accuracy. Unfortunately, the corpus creation is very expensive and time consuming task. Therefore, the motivation of this work is to propose and implement new AFR approaches that could solve this issue and perform well also with few training examples. Our approaches extend the successful method based on the Scale Invariant Feature Transform (SIFT) proposed by Aly. We propose and evaluate two methods: the Lenc-Kral matching and the SIFT based Kepenekci approach [7]. Our approaches are evaluated on two face data-sets: the ORL database and the Czech News Agency (CTK) corpus. We experimentally show that the proposed approaches significantly outperform the baseline Aly method on both corpora.

Deep Neural Networks for Czech Multi-label Document Classification

This paper is focused on automatic multi-label document classification of Czech text documents. The current approaches usually use some pre-processing which can have negative impact (loss of information, additional implementation work, etc). Therefore, we would like to omit it and use deep neural networks that learn from simple features. This choice was motivated by their successful usage in many other machine learning fields. Two different networks are compared: the first one is a standard multi-layer perceptron, while the second one is a popular convolutional network. The experiments on a Czech newspaper corpus show that both networks significantly outperform baseline method which uses a rich set of features with maximum entropy classifier. We have also shown that convolutional network gives the best results.

Local binary pattern based face recognition with automatically detected fiducial points

This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (y CTK) . We focus on popular Local Binary Patterns (LPBs) that are frequently used in this field with high recognition accuracy. One drawback of current LBP based methods is that the positions and number of the fiducial points are fixed. These points thus do not reflect the properties of a particular image whereas we believe it is beneficial to identify the most representative ones. The main contribution consists in proposing and comparing several LBP-based approaches that detect such points fully automatically. We use a set of Gabor filters for this task. Local extrema in the filter responses are detected and then used as the feature points. The number of points is further reduced by a clustering algorithm. Our approaches also differ from the other ones in the matching procedure. The proposed methods are evaluated on three standard corpora: ORL, FERET, AR face database and our y CTK dataset containing uncontrolled face images. Recognition results clearly show high quality of the proposed approaches that outperform significantly the baseline LBP approach on all corpora. The benefits of our methods are particularly evident in the case of real non-controlled images (y CTK corpus) where the accuracy is increased by more than 20% in absolute value.

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

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.

A combined SIFT/SURF descriptor for automatic face recognition

This paper deals with Automatic Face Recognition (AFR). A novel approach which combines the SIFT and SURF features for the face representation is proposed. The obtained combined SIFT/SURF descriptor is then used for face comparison by the adapted Kepenekci matching method. The proposed method is evaluated on the FERET and CTK corpora. The obtained recognition rates are 98.4% and 64.6% respectively. These recognition scores show that our approach outperforms significantly all other methods on these corpora. The differences between recognition error rates of the proposed approach and the second best one are 41% and 7% in relative value respectively.

On the effects of using word2vec representations in neural networks for dialogue act recognition

Abstract 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 embeddings 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.

Two-step supervised confidence measure for automatic face recognition

This paper deals with automatic face recognition in the context of a real application for the Czech News Agency. This system will be used to annotate people in photographs during insertion into the database. Unfortunately, the accuracy of the current face recognition approaches is limited and therefore another task to process the recognition results is very important. The main contribution of this work thus consists in proposing and evaluating a novel supervised confidence measure method as the post-processing step in order to detect incorrectly classified face images from the classifiers output. We experimentally show that the proposed confidence measure is beneficial for our application.

Confidence Measure for Czech Document Classification

This paper deals with automatic document classification in the context of a real application for the Czech News Agency (CTK). The accuracy our classifier is high, however it is still important to improve the classification results. The main goal of this paper is thus to propose novel confidence measure approaches in order to detect and remove incorrectly classified samples. Two proposed methods are based on the posterior class probability and the third one is a supervised approach which uses another classifier to determine if the result is correct. The methods are evaluated on a Czech newspaper corpus. We experimentally show that it is beneficial to integrate the novel approaches into the document classification task because they significantly improve the classification accuracy.