Vol.:(0123456789) 1 3 Journal of Ambient Intelligence and Humanized Computing https://doi.org/10.1007/s12652-020-01886-3 ORIGINAL RESEARCH Local Directional Maximum Edge Patterns for facial expression recognition V. Uma Maheswari 1  · G. Varaprasad 2  · S. Viswanadha Raju 3 Received: 14 November 2019 / Accepted: 12 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Cognitive science and neuroscience use human facial expressions of emotion. Every single facial expression can be seen at diferent passions in a face space. Nowadays, facial expression recognition and analysis is vital due to the demand of introducing advanced biometric applications in every domain space. The imperative task in facial expressions of emotion classifcation is precise feature extraction, which helps to get detailed description of facial marks. Existing feature descriptors are sufering from various problems such as intensity variations, discrimination, vulnerability etc. In this paper, propose a new feature descriptor method called LDMEP (Local Directional Maximum Edge Patterns) for facial expression analysis to overcome the hindrance. We calculated the gradients in four directions of reference pixel to elicit the more feature for better recognition instead of calculating the local diferences among neighboring pixels. We also access the orientations of the pixels then thresholded based on the dynamic threshold to avoid the featureless area calculation. Furthermore, we considered only dominant magnitude and orientation directions instead of all eight directions to generate feature. Thus, imperative and efcient features are covered in dominant positions to detect the strong edges. The paper confers that how the subsequent model can be used for the recognition of facial expression of emotion. Keywords LDMEP (Local Directional Maximum Edge Patterns) · Facial expression analysis · Feature extraction · Magnitude · Phase 1 Introduction Facial expression recognition conveys the human intention without verbal communication. It plays a vital part in numer- ous applications such as surveillance, medical area, psychol- ogy, crime analysis, mood prediction, sentiment analysis and human computer interaction. Face has the intuitive characteristics with various expressions can be recognized by the feature which is existed in the digital image. Thus, an efcient feature descriptor generation from the image is the prominent and challenging task to recognize in any cir- cumstances, however it is tedious to retrieve exact one in all situations. The factors depend on various parameters like image has taken in diferent poses, illumination, background, in various angles etc. Kotsia and Pitas (2007) facial expres- sion recognition can be done using patterns like LBP, LTP, DBC (Directional Binary Code) as local descriptors based on two factors one is local features such as color, texture, and shape etc. second is geometric based features, in con- tent based patterns feature is extracted from the image itself by fnding the relation between the pixel neighbors using derivatives so much mathematical calculation is not required to extract feature. In local features, texture based patterns are the silver lining step in facial expressions recognition, face recognition by extracting the feature available like fur- rows at eyes region, beside of nose, mouth and cheeks region on the face. In texture analysis Lucey et al. (2010) LBP is proved and notorious to recognize face and expressions due * V. Uma Maheswari umamaheshwariv999@gmail.com; umamaheswari@vardhaman.org G. Varaprasad varaprasad.cse@bmsce.ac.in S. Viswanadha Raju svraju.jntu@gmail.com 1 Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India 2 Department of Computer Science and Engineering, B.M.S. College of Engineering, Bangaluru, India 3 Department of Computer Science and Engineering, JNTUHCEJ, Jagityal, Karimnagar, India