A discriminative feature space for detecting and recognizing faces

By Abdenour Hadid, Matti Pietikäinen, Timo Ahonen. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004

334 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0b302889378874925891387fd5b97f3e3de6dcea

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xiaogang Wang
2. S. Li
3. Shengcai Liao
4. Shaogang Gong
5. Liang Zheng
6. Yi Yang
7. Dacheng Tao
8. Shuicheng Yan

Citations in the corpus

• Discriminative local binary patterns for human detection in personal album (2008, background): “in [11] for the aim of texture classification, and then extended for various fields, including face recognition ([2]), face detection ([10]), facial expression recognition [19] etc.”
• Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (2011, unknown): “It has been extensively exploited in many applications, for instance, face image analysis [3]-[4], image and video retrieval [5]-[6], environment modeling [7]-[8], visual inspection [9]-[10], motion analysis [11]-[12], biomedical and aerial image analysis [13]-[14], remote sensing [15], so forth…”
• Human detection from images and videos: A survey (2016, methodology): “The concept of local binary pattern (LBP) was originally developed for texture classification [121] and has been widely used to encode the appearance of various types of objects including human faces [53], cars [179], food [186], and human bodies [108].”
• Illumination Invariant Face Recognition Using Near-Infrared Images (2007, methodology): “Noting that the ordering relationship between pixels is not changed by any monotonic transform, we use local binary pattern (LBP) features [16], [17], [18] to compensate for the monotonic transform in the NIR images.”
• Face Description with Local Binary Patterns: Application to Face Recognition (2006, methodology): “However, a similar method has yielded in outstanding performance in f ace detection [22] and facial expression recognition [23], [24].”
• Robust facial expression recognition using local binary patterns (2005, methodology): “LBP was proposed originally for texture analysis [8, 9]; recently Ahonen et al [10, 11] presented LBP based methods for face detection and recognition.”
• Fingerprint Liveness Detection Using Convolutional Neural Networks (2016, background): “Local Binary Patterns (LBP) are a local texture descriptor that have performed well in various computer vision applications, including texture classification and segmentation, image retrieval, surface inspection, and face detection [34].”
• Automatic facial expression recognition using features of salient facial patches (2015, background): “Some earlier works [16], [17] on extraction of features from specific face regions mainly determine the facial regions which contributes more toward discrimination of expressions based on the training data.”
• Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, background): “This reenforce the superiority of LBP features in face detection and recognition in low-resolution images reported in [30].”