Table 2.

Performance Comparison of proposed framework for driver identification.

Input	Algorithm	Accuracy	FLOPs	Memory	Feature Engineering	Windowing
60 × 45	DeepConvLSTM	97.72	1.624 M	7.88 MB	Yes	Wx = 60, dx = 6
60 × 45	DeepConvGRU	95.19	1.623 M	7.88 MB	Yes	Wx = 60, dx = 6
60 × 45	DeepConvLSTM-Attention	97.86	1.632 M	7.91 MB	Yes	Wx = 60, dx = 6
60 × 45	DeepConvGRU-Attention	98.36	1.631 M	7.91 MB	Yes	Wx = 60, dx = 6
60 × 15	FCM-LSTM	95.1	0.56 M	3.28 MB	No	Wx = 60, dx = 10
60 × 15	Proposed DepthConv-LSTM	97.78	0.235 M	1.74 MB	No	Wx = 60, dx = 10
60 × 15	Proposed DepthConv- GRU	98.52	0.234 M	1.74 MB	No	Wx = 60, dx = 10
40 × 15	Proposed DepthConv-LSTM	97.86	0.233 M	1.69 MB	No	Wx = 40, dx = 6
40 × 15	Proposed DepthConv- GRU	98.72	0.232 M	1.69 MB	No	Wx = 40, dx = 6