一種基于稠密SIFT特征對齊的稀疏表達人臉識別算法
doi: 10.11999/JEIT141194
基金項目:
國家自然科學(xué)基金(61201165, 61271240, 61401228, 61403350)和南京郵電大學(xué)科研基金(NY213067)資助課題
Improved Sparse Representation Algorithm for Face Recognition Via Dense SIFT Feature Alignment
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摘要: 該文針對人臉圖像受到非剛性變化的影響，如旋轉(zhuǎn)、姿態(tài)以及表情變化等，提出一種基于稠密尺度不變特征轉(zhuǎn)換(SIFT)特征對齊(Dense SIFT Feature Alignment, DSFA)的稀疏表達人臉識別算法。整個算法包含兩個步驟：首先利用DSFA方法對齊訓(xùn)練和測試樣本；然后設(shè)計一種改進的稀疏表達模型進行人臉識別。為加快DSFA步驟的執(zhí)行速度，還設(shè)計了一種由粗到精的層次化對齊機制。實驗結(jié)果表明：在ORL，AR和LFW 3個典型數(shù)據(jù)集上，該文方法都獲得了最高的識別精度。該文方法比傳統(tǒng)稀疏表達方法在識別精度上平均提高了4.3%，同時提高了大約6倍的識別效率。
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關(guān)鍵詞:
- 人臉識別 /
- 人臉對齊 /
- 稠密尺度不變特征轉(zhuǎn)換特征 /
- 稀疏表達模型
Abstract: In order to address the non-rigid deformation (e.g., misalignment, poses, and expression) of facial images, this paper proposes a novel sparse representation face recognition algorithm using Dense Scale Invariant Feature Transform (SIFT) Feature Alignment (DSFA). The whole method consists of two steps: first, DSFA is employed as a generic transformation to roughly align training and testing samples; and then, input facial images are identified based on proposed sparse representation model. A novel coarse-to-fine scheme is designed to accelerate facial image alignment. The experimental results demonstrate the superiority of the proposed method over other methods on ORL, AR, and LFW datasets. The proposed approach improves 4.3% in terms of recognition accuracy and runs nearly 6 times faster than previous sparse approximation methods on three datasets. -
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