# Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences

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## Abstract

**:**

## 1. Introduction

- A database captured in the real-time outdoor environment using more than 50 subjects. The captured videos include a high rate of noise and background complexity.
- Refinement of the contrast of extracted video frames using the 3D box filtering approach and then fine-tuning of the ResNet101 model. The transfer-learning-based model is trained on real-time captured video frames and extracted features.
- A kurtosis-based heuristic approach is proposed to select the best features and fuse them in one vector using the correlation approach.
- Classification using multiclass one against all-SVM (OaA-SVM) and comparison of the performance of the proposed method on different feature sets.

## 2. Related Work

## 3. Proposed Methodology

#### 3.1. Videos Preprocessing

Algorithm 1: Data Augmentation Process. |

Input: Original video frame $\leftarrow \varphi \left(r,c\right)$. Output: Improved video frame $\leftarrow \tilde{\varphi}\left(r,c\right)$. Step 1: Load all video frames $\leftarrow \mathsf{\Delta}\left(\varphi \right)$. for: $1:to\leftarrow Nframes$ Step 2: Calculate filter size. - ■
- ${\tilde{F}}_{ab\tilde{ch}}={\sum}_{r=1}^{4}{\sum}_{c=1}^{4}{\sum}_{\tilde{ch}=1}^{3}\left({F}_{rc\tilde{ch}}\right)$
Step 3: Perform padding.- ■
- $Padding=\frac{{\tilde{F}}_{ab\tilde{ch}}-1}{2}$
- ■
- where horizontal padding is performed.
Step 4: Update padding window.
- ■
- $Upd\left(Padding\right)=\frac{{\sum}^{\text{}}\left(Padding\right)}{t}$
- ■
- where $t=2$
Step 5: Perform normalization.
- ■
- $Normalization=\frac{1}{{\prod}^{\text{}}\left(Upd\left(Padding\right)\right)}$
- ■
- ${\prod}^{\text{}}\leftarrow $ product of padding image.
end for |

#### 3.2. Convolutional Neural Network

#### 3.3. Deep Features Extraction

#### 3.4. Kurtosis-Controlled, Entropy-Based Feature Selection

**,**where ${f}_{k}$ denotes the length of fused feature vector.

Algorithm 2: Features selection for deep learning model 1. |

Input: Feature vector ${V}_{1}$ of dimension $N\times 2048$. Output: Selected feature ${V}_{s}$ of dimension $N\times 600$. Step 1: Features initialization. for $i\leftarrow 1toN$ // $N=2048$ Step 2: Compute kurtosis of each feature pair.- -
- $Kurtosis=\frac{\frac{{\sum}_{i=1}^{N}{\left({V}_{i}-\overline{V}\right)}^{4}}{N}}{{\left[\frac{{\sum}_{i=1}^{N}{\left({V}_{i}-\overline{V}\right)}^{2}}{N}\right]}^{2}}$ // ${V}_{i}$ is input features, $\overline{V}$ is mean feature value, and $N$ denotes the total features.
- -
- Window size is $2\times 2$ // $\sigma $ is a standard deviation.
- -
- Stride is 1.
Step 3: Newly generated kurtosis vector $\tilde{V}$ // $\tilde{V}$ is kurtosis vector. Step 4: Perform fitness function.- -
- OaA-SVM classifier.
- -
- Evaluate features.
- -
- Compute error rate.
Step 5: Repeat steps 2, 3, and 4 until error rate is minimized. end for |

Algorithm 3: Features selection for deep learning model 2. |

Input: Feature vector ${V}_{2}$ of dimension$N\times 1000$. Output: Selected feature ${V}_{s}$ of dimension$N\times 600$. Step 1: Features initialization. for $i\leftarrow 1toN$ // $N=1000$ Step 2: Compute kurtosis of each feature pair.- -
- $Kurtosis=\frac{\frac{{\sum}_{i=1}^{N}{\left({V}_{i}-\overline{V}\right)}^{4}}{N}}{{\left[\frac{{\sum}_{i=1}^{N}{\left({V}_{i}-\overline{V}\right)}^{2}}{N}\right]}^{2}}$ // ${V}_{i}$ is input features, $\overline{V}$ is mean feature value, and $N$ denotes the total features.
- -
- Window size is $2\times 2$.
- -
- Stride is 1.
Step 3: Newly generated kurtosis vector $\tilde{V}$ // $\tilde{V}=\mathrm{random}\text{}\mathrm{vector}\text{}\mathrm{size}$. Step 4: Perform fitness function.- -
- OaA-SVM classifier.
- -
- Evaluate features.
- -
- Compute error rate.
Step 5: Repeat steps 2, 3, and 4 until error rate is minimized.end for |

#### 3.5. Recognition

## 4. Results

#### 4.1. Datasets

#### 4.2. Experimental Setup

#### 4.3. Real-Time Dataset Results

#### 4.4. CASIA B Dataset Results at a 90° Angle

#### 4.5. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 7.**Sample frames collected from CASIA B dataset [44].

**Figure 10.**Confusion matrix of OaA-SVM for walking while carrying a bag at different selected angles.

**Figure 11.**Variation in computational time for selected classifiers of 4 selected angles on Real time dataset.

**Figure 12.**Confusion matrix of OaA-SVM for normal walking and walking while carrying a bag classification.

**Figure 13.**Execution time of proposed method on real-time dataset (normal walking and walking while carrying a bag).

**Figure 19.**Comparison of OvS-SVM classifier with other classification algorithms for gait recognition.

**Table 1.**Proposed gait-recognition results using real-time captured dataset for normal walk on selected angles.

Classifiers | Performance Measures | ||||||
---|---|---|---|---|---|---|---|

Recall (%) | Precision (%) | FI Score (%) | AUC | FPR | Accuracy (%) | Time (s) | |

OaA-SVM | 95.75 | 96.25 | 95.98 | 1.00 | 0.0125 | 96.0 | 204.050 |

Cubic KNN | 94.25 | 94.75 | 94.48 | 1.00 | 0.0175 | 94.5 | 366.480 |

Medium KNN | 95.00 | 95.50 | 95.24 | 1.00 | 0.0175 | 94.9 | 175.870 |

Bagged Trees | 93.50 | 93.75 | 93.62 | 0.99 | 0.0200 | 93.5 | 345.750 |

CG-SVM | 90.25 | 90.50 | 90.36 | 0.99 | 0.0325 | 90.1 | 189.300 |

Fine Tree | 86.75 | 86.50 | 86.62 | 0.92 | 0.0425 | 86.7 | 19.565 |

Medium Tree | 85.50 | 85.75 | 85.62 | 0.92 | 0.0475 | 85.4 | 39.907 |

Naïve Bayes | 84.50 | 87.25 | 85.84 | 0.90 | 0.0500 | 84.3 | 34.035 |

Coarse KNN | 61.50 | 72.50 | 66.54 | 0.92 | 0.1275 | 61.5 | 189.760 |

FG-SVM | 61.00 | 84.75 | 70.92 | 0.91 | 0.1300 | 60.9 | 132.050 |

Kernel Bayes | 48.75 | 63.50 | 50.54 | 0.73 | 0.1725 | 48.5 | 582.710 |

**Table 2.**Proposed gait-recognition results using real-time captured dataset for walking while carrying a bag at selected angles.

Classifiers | Performance Measures | ||||||
---|---|---|---|---|---|---|---|

Recall (%) | Precision (%) | FI Score (%) | AUC | FPR | Accuracy (%) | Time (s) | |

OaA-SVM | 96.5 | 97.0 | 96.7 | 1.00 | 0.01 | 96.6 | 189.850 |

Cubic KNN | 95.0 | 95.2 | 95.1 | 1.00 | 0.01 | 95.1 | 274.850 |

Medium KNN | 95.0 | 95.2 | 95.1 | 1.00 | 0.01 | 95.1 | 9.219 |

Bagged Trees | 94.7 | 95.0 | 94.8 | 0.99 | 0.01 | 94.9 | 202.010 |

CG-SVM | 90.5 | 91.0 | 90.7 | 0.99 | 0.03 | 90.5 | 137.480 |

Fine Tree | 87.7 | 81.0 | 84.2 | 0.93 | 0.04 | 87.6 | 14.190 |

Medium Tree | 86.7 | 87.0 | 86.8 | 0.93 | 0.04 | 86.8 | 13.590 |

Naïve Bayes | 83.7 | 86.7 | 85.2 | 0.89 | 0.05 | 83.7 | 20.920 |

Coarse KNN | 63.0 | 74.0 | 68.0 | 0.92 | 0.12 | 63.0 | 169.521 |

FG-SVM | 61.0 | 84.7 | 70.9 | 0.91 | 0.13 | 61.3 | 27.030 |

Kernel Bayes | 48.7 | 60.2 | 53.8 | 0.75 | 0.17 | 48.6 | 483.800 |

**Table 3.**Recognition results for all datasets with kurtosis function on 600 predictions on original deep-feature vector extracted from ResNet 101.

Classifiers | Performance Measures | ||||||
---|---|---|---|---|---|---|---|

Recall (%) | Precision (%) | FI Score (%) | AUC | FPR | Accuracy (%) | Time (s) | |

OaA-SVM | 96.5 | 96.5 | 96.5 | 1.00 | 0.03 | 96.4 | 129.830 |

Cubic KNN | 95.0 | 96.0 | 95.7 | 1.00 | 0.04 | 95.6 | 278.980 |

Medium KNN | 96.0 | 96.0 | 96.0 | 1.00 | 0.04 | 96.1 | 106.850 |

Bagged Trees | 90.5 | 90.5 | 90.5 | 0.97 | 0.09 | 90.3 | 209.610 |

CG-SVM | 82.0 | 82.5 | 82.2 | 0.91 | 0.18 | 82.2 | 102.260 |

Fine Tree | 80.0 | 80.0 | 80.0 | 0.81 | 0.20 | 79.8 | 11.954 |

Medium Tree | 76.0 | 76.5 | 76.2 | 0.80 | 0.24 | 76.3 | 30.888 |

Naïve Bayes | 68.0 | 69.0 | 68.4 | 0.78 | 0.32 | 67.8 | 18.767 |

Coarse KNN | 79.0 | 79.5 | 79.2 | 0.88 | 0.21 | 79.3 | 117.990 |

FG-SVM | 79.5 | 85.5 | 82.3 | 0.98 | 0.41 | 79.6 | 74.026 |

Kernel Bayes | 73.0 | 74.5 | 73.7 | 0.84 | 0.27 | 73.0 | 254.580 |

Classifier | Features | Measures | ||||
---|---|---|---|---|---|---|

GAP | FC | Proposed | Recall (%) | Accuracy (%) | Time (s) | |

OaA-SVM | ✓ | 90.10 | 90.22 | 242.4426 | ||

✓ | 88.52 | 88.64 | 176.4450 | |||

✓ | 95.10 | 95.26 | 114.2004 | |||

Cubic KNN | ✓ | 84.42 | 84.54 | 165.5994 | ||

✓ | 83.60 | 83.98 | 111.2011 | |||

✓ | 93.60 | 93.60 | 82.1460 | |||

Medium KNN | ✓ | 83.40 | 83.48 | 151.0014 | ||

✓ | 84.80 | 84.76 | 104.1446 | |||

✓ | 93.40 | 93.46 | 64.2914 | |||

Baggage Tree | ✓ | 85.10 | 85.16 | 256.1130 | ||

✓ | 84.14 | 84.33 | 201.0148 | |||

✓ | 87.50 | 87.45 | 117.1106 | |||

Naïve Bayes | ✓ | 71.10 | 71.04 | 171.2540 | ||

✓ | 74.94 | 74.82 | 104.3360 | |||

✓ | 79.30 | 79.30 | 76.3114 |

Gait Name | Gait Name | ||
---|---|---|---|

Normal Walk | W-Coat | W-Bag | |

Normal Walk | 94% | 4% | 2% |

W-Coat | 3% | 95% | 2% |

W-Bag | 1% | 2% | 97% |

**Table 6.**Analysis of proposed feature-selection framework on numerous feature sets. *The OvA-SVM is employed as a classifier for this table.

Dataset | Accuracy (%) on Feature Sets | |||||
---|---|---|---|---|---|---|

300 Features | 400 Features | 500 Features | 600 Features | 700 Features | All Features | |

Real-time (normal walking) | 93.70 | 94.24 | 95.35 | 96.00 | 95.70 | 93.04 |

Real-time (walking while carrying a bag) | 94.10 | 94.90 | 95.80 | 96.60 | 96.32 | 92.10 |

Real-time (normal walking vs. walking while carrying a bag) | 92.90 | 93.72 | 95.30 | 96.40 | 96.14 | 93.50 |

CASIA B Dataset | 92.96 | 93.40 | 93.85 | 95.26 | 95.10 | 92.64 |

Reference | Year | Dataset | Accuracy (%) |
---|---|---|---|

[45] | 2015 | CASIA B | 86.30 |

[46] | 2017 | CASIA B | 90.60 |

[37] | 2019 | CASIA B | 87.7 |

[6] | 2020 | CASIA B | 93.40 |

Proposed | CASIA B | 95.26 | |

Proposed | Real-time | 96.60 |

Epochs | Accuracy (%) | Error (%) | Time (min) |
---|---|---|---|

20 | 83.5 | 16.5 | 221.6784 |

40 | 87.9 | 12.1 | 375.7994 |

60 | 90.2 | 9.8 | 588.7834 |

80 | 92.6 | 5.4 | 792.5673 |

100 | 93.9 | 5.1 | 875.1247 |

150 | 96.8 | 1.2 | 988.0045 |

200 | 98.1 | 0.4 | 1105.5683 |

Epochs | Accuracy (%) | Error (%) | Time (min) |
---|---|---|---|

20 | 81.4 | 18.6 | 174.8957 |

40 | 84.6 | 15.4 | 292.0645 |

60 | 88.0 | 12 | 411.4756 |

80 | 90.2 | 9.8 | 581.8322 |

100 | 91.6 | 8.4 | 695.4570 |

150 | 94.3 | 5.7 | 808.5334 |

200 | 97.5 | 2.5 | 981.6873 |

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## Share and Cite

**MDPI and ACS Style**

Sharif, M.I.; Khan, M.A.; Alqahtani, A.; Nazir, M.; Alsubai, S.; Binbusayyis, A.; Damaševičius, R.
Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences. *Electronics* **2022**, *11*, 334.
https://doi.org/10.3390/electronics11030334

**AMA Style**

Sharif MI, Khan MA, Alqahtani A, Nazir M, Alsubai S, Binbusayyis A, Damaševičius R.
Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences. *Electronics*. 2022; 11(3):334.
https://doi.org/10.3390/electronics11030334

**Chicago/Turabian Style**

Sharif, Muhammad Imran, Muhammad Attique Khan, Abdullah Alqahtani, Muhammad Nazir, Shtwai Alsubai, Adel Binbusayyis, and Robertas Damaševičius.
2022. "Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences" *Electronics* 11, no. 3: 334.
https://doi.org/10.3390/electronics11030334