generating a first acoustic signal and projecting the generated first acoustic signal onto the presented face for probing the presented face, wherein the first acoustic signal comprises a plurality of time-limited chirps, causing a face-echo signal to be reflected from the presented face when the presented face receives an individual time-limited chirp, whereby a plurality of face-echo signals is created for the plurality of time-limited chirps;

receiving a second acoustic signal for capturing an acoustic response of the presented face due to the first acoustic signal, wherein the plurality of face-echo signals is embedded in the second acoustic signal;

preprocessing the received second acoustic signal to yield a plurality of extracted signal segments, wherein the preprocessing of the second acoustic signal includes extracting the plurality of face-echo signals from the received second acoustic signal such that an individual extracted signal segment contains a corresponding face-echo signal;

applying a Fourier transform (FT) to the individual extracted signal segment to yield a frequency segment, whereby a plurality of frequency segments for the plurality of extracted signal segments is obtained;

processing the plurality of frequency segments with a machine-learning transformer model to yield a global frequency feature of the presented face;

applying a short-time Fourier transform (STFT) to the plurality of extracted signal segments to yield a spectrogram;

processing the spectrogram with a convolutional neural network (CNN) to yield a local frequency feature of the presented face; and

combining the global and local frequency features to yield an enriched feature of the presented face for determining whether the presented face is a genuine face or a spoofer.