## 2 Methods  
### Face Detection and Alignment  
Before extracting the facial biometric features, it is required to locate the face area within the image. It is recommendable that the face area does not contain face surroundings, such as hair, clothes or background. Given the tendency towards deep neural networks to solve object recognition tasks and its good performance, a detector based on cascaded convolutional networks was used [17]. It is composed of three carefully designed deep convolutional networks fed an image pyramid to estimate face position and landmark locations in a coarse-to-fine manner, and exploits the inherent correlation between detection and alignment to increase their performance. In particular, the implementation provided by FaceNet [5] was employed. When multiple faces are present in the image, the biggest face is selected assuming that the user whose identity is validated is closer to the camera.  
Since deep learning models are sensitive to the position of the face elements within the images, once the face is detected, it is aligned to a common reference framework. To this end, some reference points belonging to eyes, nose and mouth are detected and transformed to a fixed position.  
Finally, face images must have the same size to improve the comparison performance, so all the images are resized to a common size of 160 \(\times\) 160 px.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Database** & **Main Variations** & **\# People** & **\# Images** & **ATD** & **Match Rate (\%)** \\ \hline Face Identity Preserving Features + DNN ([7]) & Multi-PIE & P, I, E, T & 337 & 128,940 & No & 90.46 \\ \hline Face Recovery + FCDN ([8]) & LFW & P, I, S, A, E, T & 5749 & 13,233 & CelebFaces & 97.27 \\ \hline Deep Supervised Autoencoders ([9]) & Ext. Yale B & I & 38 & 2432 & No & 82.22 \\  & AR & I, A & 100 & 2600 & No & 85.21 \\  & PIE & I, P, E & 68 & 5508 & No & 72.36 \\  & Multi-PIE & LT, I, V & 337 & 20,209 & No & 76.12 \\ \hline Virtual image synthesis & EK-LFH & I, B, P, V, S & 30 & 15,930 & No & 72.08 \\ + DDAN ([10]) & LFW & E, I, LT, O, V, A, P & 158 & \(>\)1580 & No & 97.91 \\ \hline JCR-ACF ([11]) & AR & I, E, A & 100 & 2600 & CASIA-WF & 96.10 \\  & Multi-PIE & P, I, E & 249 & 14,940 & CASIA-WF & 70.40 \\  & LFW & E, I, LT, O, V, A, P & 158 & \(>\)1580 & CASIA-WF & 86.00 \\  & CASIA-WF & I, P, E, A & 9175 & 406,423 & No & 15.00 \\ \hline TLFL framework ([12]) & Ext. Yale B & E & 38 & 2404 & No & 34.86 \\  & AR & E, I & 100 & 1400 & No & 55.50 \\  & PIE & P, I, E & 68 & 11,630 & No & 55.89 \\  & LFW & E, I, LT, O, V, A, P & 158 & \(>\)1422 & No & 15.21 \\  & CAS-PEAL & E & 284 & 1420 & No & 72.95 \\  & JAFFE & E & 10 & 213 & No & 89.47 \\ \hline Mean search + LSH + DNN ([13]) & Moseleb & - & \(>\)10,000 & \(>\)100,000 & No & 95.00 \\  & CASIA-WF & I, P, E, A & 10,408 & \(>\)492,744 & No & 52.43 \\ \hline DCNN ([14]) & AR & I, E, A, T & 100 & 2600 & CASIA-WF & 99.76 \\  & Ext. Yale B & P, I & 38 & 2432 & CASIA-WF & 88.30 \\  & FERET & P, E, I & 200 & 1400 & CASIA-WF & 93.90 \\  & LFW & E, I, LT, O, V, A, P & 50 & \(>\)500 & CASIA-WF & 74.00 \\ \hline NDRDF ([15]) & AR & E, I, A, T & 116 & 3016 & No & 98.00 \\ \hline Transfer Learning + KCFT ([16]) & ORL & E, I, A, T & 40 & 400 & CASIA-WF & 98.14 \\  & FERET & P, E, I & 200 & 1400 & CASIA-WF & 93.04 \\  & LFW & E, I, LT, O, V, A, P & 50 & \(>\)500 & No & 97.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of different works relating to deep learning for face recognition from a single image. ATD column shows if an additional dataset is used for training. E, I, T, LT, O, S, B, V, A and P in Variations column stand for Expression, Illumination, Time, Long Time, Orientation, Scale, Blurring, View, Accessories and Pose, respectively.