计算机技术与发展
計算機技術與髮展
계산궤기술여발전
Computer Technology and Development
2015年
11期
1-5
,共5页
人脸识别%多视角%人脸特征点定位%模型选择
人臉識彆%多視角%人臉特徵點定位%模型選擇
인검식별%다시각%인검특정점정위%모형선택
face recognition%multi-view%facial landmark localization%model selection
准确定位人脸特征点在人脸识别、三维人脸模型重建等领域都有重要作用.目前,针对正面人脸的特征点定位已经相当成熟;但是,当姿态偏转角较大时,准确定位人脸特征点依然是一个有待解决的难题.文中针对姿态偏转比较大的特征点定位,提出了一种多视角人脸特征点定位方法.在训练阶段,针对不同姿态角度,分别定义其特征点模板,并通过训练得到用于特征点搜索的CPR(级联姿态回归)模型和用于特征点模板选择的CLM (约束局部模型)模型.在测试阶段,利用每个模型分别对测试样本进行特征点搜索,然后,利用CLM模型计算各特征点模板的拟合度,选择拟合度最高的模型作为最终结果.在FERET公开库上,与当前比较先进的算法进行的对比实验表明,文中方法有效提高了较大偏转姿态下人脸特征点定位的准确性.
準確定位人臉特徵點在人臉識彆、三維人臉模型重建等領域都有重要作用.目前,針對正麵人臉的特徵點定位已經相噹成熟;但是,噹姿態偏轉角較大時,準確定位人臉特徵點依然是一箇有待解決的難題.文中針對姿態偏轉比較大的特徵點定位,提齣瞭一種多視角人臉特徵點定位方法.在訓練階段,針對不同姿態角度,分彆定義其特徵點模闆,併通過訓練得到用于特徵點搜索的CPR(級聯姿態迴歸)模型和用于特徵點模闆選擇的CLM (約束跼部模型)模型.在測試階段,利用每箇模型分彆對測試樣本進行特徵點搜索,然後,利用CLM模型計算各特徵點模闆的擬閤度,選擇擬閤度最高的模型作為最終結果.在FERET公開庫上,與噹前比較先進的算法進行的對比實驗錶明,文中方法有效提高瞭較大偏轉姿態下人臉特徵點定位的準確性.
준학정위인검특정점재인검식별、삼유인검모형중건등영역도유중요작용.목전,침대정면인검적특정점정위이경상당성숙;단시,당자태편전각교대시,준학정위인검특정점의연시일개유대해결적난제.문중침대자태편전비교대적특정점정위,제출료일충다시각인검특정점정위방법.재훈련계단,침대불동자태각도,분별정의기특정점모판,병통과훈련득도용우특정점수색적CPR(급련자태회귀)모형화용우특정점모판선택적CLM (약속국부모형)모형.재측시계단,이용매개모형분별대측시양본진행특정점수색,연후,이용CLM모형계산각특정점모판적의합도,선택의합도최고적모형작위최종결과.재FERET공개고상,여당전비교선진적산법진행적대비실험표명,문중방법유효제고료교대편전자태하인검특정점정위적준학성.
Facial landmark localization plays an important role in many face-related applications such as face recognition and 3D face re-construction. Although existing methods already achieve promising results on frontal and near-frontal face images,their performance on face images with large pose angles is still far from being satisfactory. A multi-view facial landmark localization method is proposed in this paper. It divides head pose angles into a number of non-overlapping ranges. During training,for each range of head pose angles,a facial landmark template is constructed by using the CPR (Cascaded Pose Regression) method,and a corresponding texture model is established by using the CLM (Constrained Local Model) method. During testing,given a new face image,all the templates are applied to it,each resulting in a set of facial landmarks. The associated texture models are then used to compute the fitness values of them,from which the one with the maximum fitness is chosen as the final result. Experiments on the FERET database with comparison to a state-of-the-art method prove the effectiveness of the proposed method in localizing the facial landmarks on face images with large pose angles.