Online vision-based eye detection: LBP/SVM vs LBP/LSTM-RNN

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Benrachou, DE (Author) Other; Filipe Neves Santos (Author) FEUP; Boulebtateche, B (Author) Other; Bensaoula, S (Author) Other

Conference proceedings International

Title: Lecture Notes in Electrical Engineering

Pages: 659-668

11th Portuguese Conference on Automatic Control, CONTROLO 2014

Porto, 21 July 2014 through 23 July 2014

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-009-TQY

DOI: 10.1007/978-3-319-10380-8_63

Abstract (EN): Eye detection is a complex issue and widely explored through several applications, such as human gaze detection, human-robot interaction and driver's drowsiness monitoring. However, most of these applications require an efficient approach for detect the ocular region, which should be able to work in real time. In this paper, it is proposed and compare two approaches for online eye detection. The proposed schemes, work under real variant illumination conditions, using the conventional appearance method that is known for its discriminative power especially in texture analysis. In the first stage, the salient eye features are automatically extracted by employing Uniform Local Binary pattern (LBP) operator. Thereafter, supervised machine learning methods are used to classify the presence of an eye in image path, which is described by an LBP histogram. For this stage, two approaches were tested; Support Vector Machine and Long Short-Term Memory Recurrent Neural Network, both are trained for discriminative binary classification, between two classes namely eye / non eye. The human eyes were successfully localized in real time videos, which were obtained from a laptop with uncalibrated web camera. In these tests, different people were considered and light illumination. The experimental results are reported. © 2015 Springer International Publishing.

Language: English

Type (Professor's evaluation): Scientific