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Road sign recognition system remains a challenging part of designing an Intelligent Driving Support System. While there exist many approaches to classify road signs, none have adopted an unsupervised approach. This paper proposes a way of Self-Organizing feature mapping for recognizing a road sign. The emergent self-organizing map (ESOM) is employed for the feature mapping in this study. It has the capability of visualizing the distance structures as well as the density structure of high-dimensional data sets, in which the ESOM is suitable to detect non-trivial cluster structures. This paper discusses the usage of ESOM for road sign detection and classification. The benchmarking against some other commonly used classifiers was performed. The results demonstrate that the ESOM approach outperforms the others in conducting the same simulations of the road sign recognition. We further demonstrate that the result obtained with ESOM is significantly more superior than traditional SOM which does not take into the boundary effect like ESOM did. [ABSTRACT FROM AUTHOR]

This paper describes a novel structural approach to recognize the human facial features for emotion recognition. Conventionally, features extracted from facial images are represented by relatively poor representations, such as arrays or sequences, with a static data structure. In this study, we propose to extract facial expression features vectors as Localized Gabor Features (LGF) and then transform these feature vectors into FacE Emotion Tree Structures (FEETS) representation. It is an extension of the Human Face Tree Structures (HFTS) representation presented in (Cho and Wong in Lecture notes in computer science, pp 1245–1254, ). This facial representation is able to simulate as human perceiving the real human face and both the entities and relationship could contribute to the facial expression features. Moreover, a new structural connectionist architecture based on a probabilistic approach to adaptive processing of data structures is presented. The so-called probabilistic based recursive neural network (PRNN) model extended from Frasconi et al. (IEEE Trans Neural Netw 9:768–785, ) is developed to train and recognize human emotions by generalizing the FEETS representation. For empirical studies, we benchmarked our emotion recognition approach against other well known classifiers. Using the public domain databases, such as Japanese Female Facial Expression (JAFFE) (Lyons et al. in IEEE Trans Pattern Anal Mach Intell 21(12):1357–1362, ; Lyons et al. in third IEEE international conference on automatic face and gesture recognition, ) database and Cohn–Kanade AU-Coded Facial Expression (CMU) Database (Cohn et al. in 7th European conference on facial expression measurement and meaning, ), our proposed system might obtain an accuracy of about 85–95% for subject-dependent and subject-independent conditions. Moreover, by testing images having artifacts, the proposed model significantly supports the robust capability to perform facial emotion recognition. [ABSTRACT FROM AUTHOR]

This paper describes a novel method of facial representation and recognition based upon adaptive processing of tree structures. Instead of the conventional flat vector representation for a face, a neural network approach-based technique is proposed to transform the Localised Gabor Feature (LGF) vectors extracted from human facial components into Human Face Tree Structure (HFTS) to represent a human face. A structural training algorithm is assigned to train and recognize the face identity in this HFTS representation with the corresponding LGF vectors. By benchmarking using the tested public face databases presented in this paper, our approach is able to achieve accuracy up to 90% under different scenarios of lighting conditions and posture orientations. [ABSTRACT FROM AUTHOR]

A generalized neural reflectance (GNR) model for enhancing face recognition under variations in illumination and posture is presented in this paper. Our work is based on training a number of synthesis images of each face taken at single lighting direction with frontal/posture view. This way of synthesizing images can be used to build training cases for each face under different known illumination conditions from which face recognition can be significantly improved. However, reconstructing face shape may not easily be achieved and the human face images usually form by highly complex structure which suffers from strong specular and unknown reflective conditions. In this paper, these limitations are addressed by Cho and Chow (IEEE Trans Neural Netw 12(5):1204–1214, 2002). Face surfaces are recovered by this GNR model and face images in different poses are synthesized to create a database for training. Our training algorithm assigns to recognize the face identity by similarity measure on face features extracting first by the principle component analysis (PCA) method and then further processing by the Fisher’s discrimination analysis (FDA) to acquire lower dimensional patterns. Experimental results conducted on the Yale Face Database B show that lower error rates of classification and recognition are achieved under different variations in lighting and pose and the performance significantly outperforms the recognition without using the proposed GNR model. [ABSTRACT FROM AUTHOR]