2.2.4. RF

RF [32] is a face detector based on Discriminative Response Map Fitting (DRMF), which is a specific face fitting technique. DRMF is a discriminative regression method for the Constrained Local Models (CLMs) framework. Precision is adjusted in RF using the sensitivity parameter *s* that sets both a lower and a higher sensitivity value.

#### 2.2.5. NPD

NPD [33] extracts the illumination and blur invariant NPD features mentioned in the Introduction. NPD is computed as the difference-to-sum ratio between two pixels and is extremely fast because it requires only one memory access using a lookup table. However, because NPD contains redundant information, AdaBoost is applied to select the most discriminative feature set and to construct strong classifiers. The Gentle AdaBoost algorithm [60] is adopted for the deep quadratic trees. The splitting strategy consists in quantizing the feature range into *l* discrete bins (*l* = 256 in the original paper and here), and an exhaustive search is performed to determine whether a feature lies within a given range [θ1, θ2]. The weighted mean square error is applied as the optimal splitting criterion.
