*5.1. Technique Presentation*

The hybrid approaches are based on local and subspace features in order to use the benefits of both subspace and local techniques, which have the potential to offer better performance for face recognition systems.


evaluate the identification performance of MM-DFR. The flowchart of the proposed MM-DFR framework is shown in Figure 12.


**Figure 12.** Flowchart of the proposed multimodal deep face representation (MM-DFR) technique [95]. CNN, convolutional neural network.

**Figure 13.** The proposed CNN–LSTM–ELM [103].

#### *5.2. Summary of Hybrid Approaches*

Table 3 summarizes the hybrid approaches that we presented in this section. Various techniques are introduced to improve the performance and the accuracy of recognition systems. The combination between the local approaches and the subspace approach provides robust recognition and reduction of dimensionality under different illumination conditions and facial expressions. Furthermore, these technologies are presented to be sensitive to noise, and invariant to translations and rotations.


Ding et al. [95]

 CNNs and SAE

 LFW

 \_ \_

 Complexity

 High recognition rate

 99%

OCLBP,over-completeLBP;WCCN,withinclasscovariancenormalization;WLBP,WalshLPB;ICP,iterative

#### **6. Assessment of Face Recognition Approaches**

In the last step of recognition, the face extracted from the background during the face detection step is compared with known faces stored in a specific database. To make the decision, several techniques of comparison are used. This section describes the most common techniques used to make the decision and comparison.
