In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.

Facial age estimation requires accurately capturing the mapping relationship between facial features and corresponding ages, so as to precisely estimate ages for new input facial images. Previous works usually use one-layer regression model to learn this complex mapping relationship, resulting in low estimation accuracy. In this letter, we propose a new gender-specific regression model with a two-layer structure for more accurate age estimation. Different from recent two-layer models that use a global regressor to calculate cumulative attributes (CA) and use CA to estimate age, we use gender-specific ones to calculate CA with more flexibility and precision. Extensive experimental results on FG-NET and Morph 2 datasets demonstrate the superiority of our method over other state-of-the-art age estimation methods.