Spontaneous Emotional Facial Expression Detection

Spontaneous Emotional Facial Expression Detection

Abstract • 主旨 • 在自然的對話的情境下，偵測臉部情緒的發生。 • 實驗設計 • 自然對話的情境是採用Adult Attachment Interview (AAI)，蒐集成人在描述童年經驗時的臉部表情、生理訊號及自我表述的情感。

Abstract • 臉部偵測設備 • 3D face tracker used to extract facial textures • 數據分析 • one-class classification, including kernel whitening and Support Vector Data Description (SVDD)

ADULT ATTACHMENT INTERVIEW DATA • 成人依戀訪談（AAI）是一種用來描述個人的半結構化面試, 主要討論受測者目前的心態與過去親子經驗。訪談中要求受測者形容他們早期和目前與父母的關係。 • 攝影機被藏起來，不被受測者查覺

3D FACE TRACKER • Piecewise Bezier Volume Deformation (PBVD) tracker

ONE-CLASS CLASSIFICATION • Kernel Whitening • Support Vector Data Description (SVDD)

Data Preparation(1) • 刪除無用的資料 • 例如手擋住臉、臉超出攝影機畫面、臉轉向大於40度角等 • 編碼方式 • Each emotion sequence starts from and to the emotion intensity scoring scale B (slight) or C (marked pronounced).

Data Preparation(1) • 資料縮減 • The total number of sequences is 150 (69 positive states, 36 negative states, and 45 non-emotional states) for the female participant. • 130 for the male participant (30 positive states, 56 negative states, and 45 non-emotional states).

Data Preparation(2) • 3D face tracker

Recognition • validation set • ten-fold cross-validation • one-class classifiers • Gaussian • Kernel whitening + SVDD (KSVDD) • SVDD

Comparison between One-class and Two-class Classifiers

Advantage • Detecting emotional facial expressions occurred in a realistic human onversation setting—the Adult Attachment Interview (AAI) • AAI data suggest that one-class classification methods can reach a good balance between cost and recognition performance.

Spontaneous Emotional Facial Expression Detection