SMILE AND LAUGH EXPRESSIONS DETECTION BASED ON LOCAL MINIMUM KEY POINTS

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MOHAMMADI DASHTI MINA; HAROUNI MAJID

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Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2018 | Volume:15 | Issue:2 (SERIAL 36) Page(s): 69-87

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Information Journal Paper

Title

SMILE AND LAUGH EXPRESSIONS DETECTION BASED ON LOCAL MINIMUM KEY POINTS

Author(s)

MOHAMMADI DASHTI MINA | HAROUNI MAJID | Issue Writer Certificate

Keywords

LOCAL KEY POINTS EXTRACTIONQ1

FACIAL EXPRESSION DETECTIONQ1

CORNER DETECTORQ3

DESCRIPTOR ALGORITHMQ1

DIMENSION REDUCTIONQ1

Abstract

In this paper, a smile and laugh facial expression is presented based on DIMENSION REDUCTION and description process of the key points. The paper has two main objectives; the first is to extract the local critical points in terms of their apparent features, and the second is to reduce the system’ s dependence on training inputs. To achieve these objectives, three different scenarios on extracting the features are proposed. First of all, the discrete parts of a face are detected by local binary pattern method that is used to extract a set of global feature vectors for texture classification considering various regions of an input-image face. Then, in the first scenario and with respect to the correlation changes of adjacent pixels on the texture of a mouth area, a set of local key points are extracted using the Harris CORNER DETECTOR. In the second scenario, the DIMENSION REDUCTION of the extracted points of first scenario provided by principal component analysis algorithm leading to reduction in computational costs and overall complexity without loss of performance and flexibility; and in the final scenario, a set of critical points is extracted through comparing the extracted points’ coordinates of the first scenario and the BRISK Descriptor, which is utilized a neighborhood sampling strategy of directions for a key-point. In the following, without training the system, facial expressions are detected by comparing the shape and the geometric distance of the extracted local points of the mouth area. The well-known standard Cohn-Kaonde, CAFÉ , JAFFE and Yale benchmark dataset are applied to evaluate the proposed approach. The results shows an overall enhancement of 6. 33% and 16. 46% for second scenario compared with first scenario and third scenario compared with second scenario. The experimental results indicate the power efficiency of the proposed approach in recognizing images more than 90 % across all the datasets.

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APA: Copy

MOHAMMADI DASHTI, MINA, & HAROUNI, MAJID. (2018). SMILE AND LAUGH EXPRESSIONS DETECTION BASED ON LOCAL MINIMUM KEY POINTS. SIGNAL AND DATA PROCESSING, 15(2 (SERIAL 36) ), 69-87. SID. https://sid.ir/paper/160931/en

Vancouver: Copy

MOHAMMADI DASHTI MINA, HAROUNI MAJID. SMILE AND LAUGH EXPRESSIONS DETECTION BASED ON LOCAL MINIMUM KEY POINTS. SIGNAL AND DATA PROCESSING[Internet]. 2018;15(2 (SERIAL 36) ):69-87. Available from: https://sid.ir/paper/160931/en

IEEE: Copy

MINA MOHAMMADI DASHTI, and MAJID HAROUNI, “SMILE AND LAUGH EXPRESSIONS DETECTION BASED ON LOCAL MINIMUM KEY POINTS,” SIGNAL AND DATA PROCESSING, vol. 15, no. 2 (SERIAL 36) , pp. 69–87, 2018, [Online]. Available: https://sid.ir/paper/160931/en

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