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Home / programming language / Online Matlab Projects / Local Directional Ternary Pattern for Facial Expression Recognition
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Local Directional Ternary Pattern for Facial Expression Recognition

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SKU: PROJ8287 Categories: 2018-2019 Projects, Digital Image Processing Projects, Online Matlab Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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Local Directional Ternary Pattern for Facial Expression Recognition

Abstract-In the first place, presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features ( i.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based  methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two level grid to construct the face descriptor while sampling expression-related information at different scales. We use a coarse grid for stable codes (highly related to non-expression), and a finer one for active codes (highly related to expression). This multi-level approach enables us to do a finer grain description of facial motions, while still characterizing the coarse features of the expression. Moreover, we learn the active LDTP codes from the emotionrelated facial regions. Here tested our method by using persondependent and independent cross-validation schemes to evaluate the performance. We show that our approaches improve the overall accuracy of facial expression recognition on six datasets.

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